Project Report on
LOW COST CONDITION MONITORING OF BEARINGS USING VIBRATION ANALYZER
VISVESVARAYA TECHNOLOGICAL UNIVERSITYJnana Sangama, Belgaum, Karnataka
In partial fulfillment of the requirements for the award of the Degree of
BACHELOR OF ENGINEERING in AUTOMOBILE ENGINEERING
Under the guidance ofMurgayya.S.BDept of Automobile Engineering,DAYANANDA SAGAR COLLEGE OF ENGINEERING, BANGALORE
Department of Automobile Engineering
Dayananda Sagar College of EngineeringSHAVIGE MALLESHWARA HILLS, KUMARASWAMY LAYOUT, BANGALORE-782017-2018
Dayananda Sagar College of EngineeringShavige Malleshwara Hills, Kumaraswamy Layout, Bangalore-560078
Department of Automobile Engineering
Certified that the Project Work entitled “LOW COST CONDITION MONITORING OF BEARINGS USING VIBRATION ANALYZER” is a bonafide work carried out by Aditya Vikraman (1DS14AU005), Abhijit.V (1DS14AU001), Ashish Samaga (1DS14AU008), Kishore.N (1DS14AU021), in partial fulfillment for the award of Bachelor of Engineering in Automobile Engineering of the Visvesvaraya Technological University, Belgaum during the year 2017-18. It is certified that all the corrections/suggestions indicated for internal assessment have been incorporated in the report submitted in the department library. The Project Report has been approved as it satisfies the academic requirements in respect of Project Work prescribed for the said degree.
Signature of Guide Signature of HOD Signature of Principal MURGAYYA.S.B DR.H.N.SURESH DR.C.P.S PRAKASH
Sl.No. Name of the Examiner Signature with Date
We, Aditya Vikraman (1DS14AU005), Abhijit.V (1DS14AU001), Ashish Samaga (1DS14AU008), Kishore.N (1DS14AU021), hereby declare that the project work entitled “LOW COST CONDITION MONITORING OF BEARINGS USING VIBRATION ANALYZER” has been independently carried out by us under the guidance of Murgayya.S.B, Professor, Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, in partial fulfillment of the requirements of the degree of Bachelor of Engineering in Automobile Engineering of Visvesvaraya Technological University, Belgaum.
We further declare that we have not submitted this report to any other university for the reward of any degree.
1. Aditya Vikraman (1DS14AU005)
2. Abhijit.V (1DS14AU001)
3. Ashish Samaga (1DS14AU008)
4. Kishore.N (1DS14AU021)
Table of Contents
Aluminium Alloy 6063(H9)
POWER SPECTRUM DENSITY OF A SIGNAL
Causes for the occurrence of Rust
Causes of Pear Skin
Wear and Fretting
Results and Discussion
Application and Future Scope
The satisfaction and bliss accompanying the successful completion of my project would be incomplete without the mention of the people who made it possible.
My gratitude also goes to our HOD Dr. H. N. Suresh, Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, for his sincere advice and encouragement. He always lifted my sagging spirit when I faced problems and helped me reach the shore of success.
I also take this opportunity to thank our Principal C.P.S Prakash, for his support in all the academic matters.
I would like to show my greatest appreciation to Murgayya S. B., Associate Professor, Department of Automobile Professor Engineering, Dayananda Sagar College of Engineering, Bangalore. I can’t say thank you enough for his tremendous support and help. I feel motivated and encouraged every time I met him. Without his encouragement and guidance this work would not have materialized.
I also owe my gratitude to and all the others teaching and non-teaching staff members for their rendering co-operation.
Finally, I thank my parents, my family members, all my friends who inspired, motivated and supported me throughout the course of work, every hand that rendered help directly or indirectly, and every heart that blessed me, for which I sincerely thank them.
Machine condition monitoring has grown progressively over the years and has become an essential component in the today’s industrial outfield. A cost effective machine condition monitoring system is need of the hour for predictive maintenance. Minimum effort has been exerted on monitoring equipment using smart phone technology. In this report, we have discussed a condition monitoring system using smart phone. Owing to rapidly growing smart-phone market in scalability and computational power, it is possible to achieve a cost-effective monitoring device. The capability of the smartphones to capture the vibrations using an internal built-in accelerometer has supported us to propose an accurate and efficient vibration condition monitoring system with the ability to acquire data, capture the fault signatures, and determine the presence of the fault in the working model.
In different aspects of mechanical engineering, shafts are commonly used as structural members and are subjected to static and dynamic loads. The minute cracks produced during the development phase, can coalesce and develop into larger cracks and subsequently grow to critical size. This increase in size promotes a drastic change in the operational control limits which can lead to failure. Since the application of rotating shaft is vast in the current engineering fields, its maintenance and timely fault detection is highly critical. Condition monitoring is the process of monitoring a parameter of a particular condition in machinery (vibration, temperature etc.), in order to identify a significant change which is responsible for the indication of a developing fault. It is a major component of predictive maintenance. The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences. Machine condition monitoring (MCM) is crucial in all industrial processes to achieve high reliability, improved maintenance, reduced man power. Condition Monitoring specifically deals with the detection and diagnosis of the abnormalities surfaced during the machine’s working period. To diagnose the abnormality, it is important to take into account the physical parameters which vary according to the variation in the operation of the machine and ‘vibration’ is one of such parameters. Among different physical parameters such as vibrations, temperature, thermal imaging etc, vibration signatures are the most suitable for fault diagnosis. These control limits for a standard are established by vibrational characteristics such as frequency, amplitude and are considered during fault detection. Condition monitoring enables us to provide a higher standard of safety by preventing the occurrences of sudden failures and facilitates a reduction in costly unscheduled machine down time. Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure. Condition monitoring techniques are normally used on rotating equipment, auxiliary systems and other machinery (electric motors, compressors, pumps). Periodic Inspection is carried out using Non Destructive Testing techniques. The following list includes the main condition monitoring techniques applied in the industrial and transportation sectors:
Vibration Analysis and diagnostics
The new advances in the field of condition based maintenance has reduced the trend towards reactive maintenance in which the maintenance cost is five times higher than well planned and schedule maintenance. Though the condition monitoring devices are becoming more and more popular and cost is decreasing accordingly, still it is one of the vital considerations for the industrial investors and the solutions providers to reduce the cost of the monitoring equipment.
1.1 Lubricant Analysis
Lubricant analysis is also referred to as Oil analysis (OA). It is considered as a laboratory analysis of a lubricant’s properties, suspended contaminants, and wear debris. It is performed under the routine predictive maintenance to provide meaningful and accurate information on lubricant and machine condition. By tracking oil analysis sample results over the life of a particular machine, trends can be established which can help eliminate costly repairs. The study of wear in machinery is called tribology. Tribology is the science and engineering of interacting surfaces in relative motion. It includes the study and application of the principles of friction, lubrication and wear. Tribology is highly interdisciplinary. It draws on many academic fields, including physics, chemistry, materials science, biology and engineering.
OA can be divided into three categories:
Analysis of oil properties including those of the base oil and its additives,
Analysis of contaminants,
Analysis of wear debris from machinery.
In addition to monitoring oil contamination and wear metals, modern usage of OA includes the analysis of the additives in oils to determine if an extended drain interval may be used. Maintenance costs can be reduced using OA to determine the remaining useful life of additives in the oil. By comparing the OA results of new and used oil, the necessity for an oil change can be questioned.
1.2 Acoustic Emission
Acoustic emission (AE) is the phenomenon that when acoustic (elastic) waves are emitted by solids when it undergoes irreversible changes in its internal structure, for example as a result of crack formation or plastic deformation due to aging, temperature gradients or external mechanical forces. In particular, AE is occurring during the processes of mechanical loading of materials and structures accompanied by structural changes that generate local sources of elastic waves. This results in small surface displacements of a material produced by elastic or stress waves generated when the accumulated elastic energy in a material or on its surface is released rapidly. The waves generated by sources of AE are of practical interest in structural health monitoring (SHM), quality control, and system feedback, process monitoring and other fields. In SHM applications, AE is typically used to detect, locate and characterize damage. AE tools are designed for monitoring acoustic emissions produced by the material during failure or stress, and not on the material’s effect on externally generated waves. The three major applications of AE techniques are:
1) Source location – determine the locations where an event source occurred;
2) Material mechanical performance – evaluate and characterize materials/structures;
3) Health monitoring – monitor the safe operation of a structure, bridges, pressure containers.
The technique is used to study the formation of cracks during the welding process, as opposed to locating them after the weld has been formed with the more familiar ultrasonic testing technique. In a material under active stress, such as some components of an airplane during flight, transducers mounted in an area can detect the formation of a crack at the moment it begins propagating. A group of transducers can be used to record signals, then locate the precise area of their origin by measuring the time for the sound to reach different transducers.
1.3 Infrared Thermography
Temperature is one of the most common indicators of the structural health of equipment and components. Faulty machineries, corroded electrical connections, damaged material components, etc., can cause abnormal temperature distribution. By now, infrared thermography (IRT) has become a matured and widely accepted condition monitoring tool where the temperature is measured in real time in a non-contact manner. IRT enables early detection of equipment flaws and faulty industrial processes under operating condition thereby, reducing system down time, catastrophic breakdown and maintenance cost. Last three decades witnessed a steady growth in the use of IRT as a condition monitoring technique in civil structures, electrical installations, machineries and equipment, material deformation under various loading conditions, corrosion damages and welding processes. IRT has also found its application in nuclear, aerospace, food, paper, wood and plastic industries. With the advent of newer generations of infrared camera, IRT is becoming a more accurate, reliable and cost effective technique.
1.4 Ultrasound Testing
These include techniques based on the propagation of ultrasonic waves in the object or material tested. In most common UT applications, very short ultrasonic pulse-waves with centre frequencies ranging from 0.1-15 MHz, and occasionally up to 50 MHz, are transmitted into materials to detect internal flaws or to characterize materials. A common example is ultrasonic thickness measurement, which tests the thickness of the test object, for example, to monitor pipework corrosion.
Ultrasonic testing is often performed on steel and other metals and alloys, though it can also be used on concrete, wood and composites, albeit with less resolution. It is used in many industries including steel and aluminium construction, metallurgy, aerospace, automotive and other transportation sectors.
In ultrasonic testing, an ultrasound transducer connected to a diagnostic machine is passed over the object being inspected. The transducer is typically separated from the test object by a couplant (such as oil) or by water, as in immersion testing. However, when ultrasonic testing is conducted with an Electromagnetic Acoustic Transducer (EMAT) the use of couplant is not required.
There are two methods of receiving the ultrasound waveform: reflection and attenuation. In reflection (or pulse-echo) mode, the transducer performs both the sending and the receiving of the pulsed waves as the “sound” is reflected back to the device. Reflected ultrasound comes from an interface, such as the back wall of the object or from an imperfection within the object. The diagnostic machine displays these results in the form of a signal with an amplitude representing the intensity of the reflection and the distance, representing the arrival time of the reflection. In attenuation (or through-transmission) mode, a transmitter sends ultrasound through one surface, and a separate receiver detects the amount that has reached it on another surface after traveling through the medium. Imperfections or other conditions in the space between the transmitter and receiver reduce the amount of sound transmitted, thus revealing their presence. Using the couplant increases the efficiency of the process by reducing the losses in the ultrasonic wave energy due to separation between the surfaces.
1.5 Vibrational Analysis
Vibration Analysis (VA), applied in an industrial or maintenance environment aims to reduce maintenance costs and equipment downtime by detecting equipment faults. VA is a key component of a Condition Monitoring (CM) program, and is often referred to as Predictive Maintenance. Most commonly VA is used to detect faults in rotating equipment (Fans, Motors, Pumps, and Gearboxes etc.) such as Unbalance, Misalignment, rolling element bearing faults and resonance conditions.
VA can use the units of Displacement, Velocity and Acceleration displayed as a Time Waveform (TWF), but most commonly the spectrum is used, derived from a Fast Fourier Transform of the TWF. The vibration spectrum provides important frequency information that can pinpoint the faulty component.
2. Literature Review
2.1 Ali Vaziri, Prof. M.J. Patil; Vibration Analysis of Cracked Shaft.The test rig as shown in fig 1, consists of a shaft placed between two bearing with sensors located on the bearing A. The shaft is run by a dc s hunt motor through a flexible coupling. The setup is initially operated to ensure smooth operation and dampen down the minor vibrations. The accelerometer is used along with a FFT analyzer to obtain the vibration signal. These signals are obtained for different speeds. The standard reading was obtained using FFT analyzer by operating the test rig with standard shaft without cracks. Then a crack was introduced in the shaft at a distance of 5cm from bearing B and the reading interpreted by the software was compared with that of the original values. The table below shows the RMS acceleration for 0, 2 and 4mm crack depth at different speeds. From fig 2, it is observed that the overall RMS acceleration increases with increase in the depth of the crack or speed of rotation of the shaft.
Fig 2.1: Experimental set up components 1
Fig 2: Vibration Magnitude (RMS value) ?m/s2Vs. Speed (frequency) rpm plot
2.2 Asmita G. Fulzele, V.G Arajpure, P.P. Holay, N.M Patil; Condition Monitoring of Shaft of Single-Phase Induction Motor using Optical Sensor.
This method was utilized for the detection of the faults in bearings, unwanted play between bush and shaft, resulting in the generation of a swirl motion. This motion is resolved into rotary and oscillatory components. Transfer Path Analysis is a method to identify and resolve the various Vibrational problems. In fig.5, the Transfer Path Analysis is used to determine the path of the vibrations occurring, due to the presence of faults, and help understand the dominant cause producing the vibrations. The vibrations produced due to faults are attenuated, damped or amplified which results in the loss of valuable information regarding fault genesis. Hence, for Condition Monitoring of rotating shafts, an optical sensor is used. Contact Type Sensors like Electromagnetic and Piezo-Electrical sensors cannot be mounted on the rotating shafts and are hence inapplicable. Optically Sensitive Reflection Monitoring Technique work on the principle that when a beam of light is incident upon a vibrating body, the change in the reflected light help determine the fault patterns and characteristics. The reflected light falling upon the sensor is converted to voltage. As seen in fig.4, the voltage generated at the sensor will help determine the fault displacement and it’s localization along the length of the rotating machine. The reflecting surfaces of the vibrating material affect the output characteristics of the signal, posing various difficulties to the technique. This technique is widely used for the inspection of faulty shafts and prepares an accurate diagnostic chart. 2
Fig 2.2 Whirl motion. The locus of blue line indicates the path followed by the shaft vibrations.
Fig 2.3 Critical comparison between accelerometer and optical sensor 2
2.3 Sheng Zhang, Joseph Mathew, Lin Ma, Yong Sun and Avin Mathew; Statistical Condition Monitoring based on vibration Signals.
In this experimental setup as shown in fig 2.4, a test rig is used for the detection of misalignment of a rotating shaft of varying magnitude. 3 This misalignment introduces a force on the support bearing during the working period. During high operational speed the force occurring due to misalignment increases, resulting in higher wear on the bearing. This wear has a direct impact on the life of the bearing whose decrement is proportional to the cube of every force increment 4. The set up consists of a shaft with a wheel supported by two bearing, the former being operated by a motor through a pair of flexible coupling. One of the bearing is mounted on a screw mechanism to introduce a misalignment of 0.5 and 1.5mm at the coupling. The data is collected by a system consisting of a contact type piezoelectric accelerometer, a signal conditioner, a band pass filter along with a laptop supporting a data acquisition application software. The accelerometer mounted on the bearing provides data at different position of the shaft misalignment. The shaft is made to run at a constant speed for five rotations along each of the misaligned shaft positions. The signal obtained at the ‘zero’ misalignment position is taken as the standard for comparison.
Fig 2.4. Test rig for the detection of misalignment.
2.4 Khadersab A, Dr. Shivakumar S.; Vibration Analysis Techniques for Rotating Machinery and its effect on Bearing Faults.
The defects occurring in the bearings are mainly classified into two types, namely localized and distributed defects. This categorization is mainly done in accordance with the frequency domain established within the vibration spectrum. 5 The faults that come about on the inner and outer race of the bearings can be analyzed by three different methods; analytical, experimental and numerical simulation method 6-12. In analytical method, the frequency characteristics are mainly dependent on factors pertaining to the dimensional accuracies, speed and the ball count. The acquired frequency curves are characterized into fundamental frequencies to distinguish between the inner and outer race. This is in turn compared to the frequency characteristics obtained using the vibration analysis technique. A Piezoelectric accelerometer is located on the bearing casing is used to determine the various vibration parameters on the time-frequency spectrum. The Data Acquisition system converts the signals obtained by the accelerometer during operation into digital numeric values. This data can be interpreted with powerful Fast Fourier Transform (FFT) algorithms that can be used to establish a frequency range. The FFT software contains a broad complex range of frequency spectrum. Using the FFT signals the Inverse Fast Fourier Transfer (IFFT) and spectrogram is obtained. Similarly, the IFFT and spectrogram obtained from the healthy and defective inner and outer race of a bearing are compared for fault indications.
5. Muhammad Farrukh Yaqub, Iqbal Gondal; Smart phone based vehicle condition monitoring.The accelerometer in the smart phone has very limited capacity in terms of sampling rate for data acquisition. It is a key requirement for the data acquisition unit to capture the vibrations dataset minimally at twice the maximum defect frequency to be monitored to avoid aliasing. This paper proposes an enhanced sampling rate ESR technique for capturing the data at an improved sampling rate in spite of device limitation. ESR captures the vibration by maintaining a phase relation between the vibration samples acquired between different revolutions. Meaning, if a device captures eight samples for one rotation of the shaft (8 sample/rotation) and during the next rotation the samples are collected after a precise time delay, then eight additional samples are obtained. If the two samples are merged corresponding to the indices of the two rotations, then the data has been captured at 16 sample/rotation thus enhancing the resolution by filling the gap between each of the eight sample acquired.
Fig.2.5 Spectral analysis with normal device sampling rate
Fig. 2.6 Spectral analysis with enhanced sampling rate
Figure 2.5 shows the spectral contents of the simulated vibration data when acquired at 50samples/sec (sampling rate of the Smartphone) and ESR is not applied. In Fig. 2.5, we can only read the range of the resonant frequency band (3000Hz- 4000Hz) but does not provide any details for the bearing faults with a defect frequency as 150Hz. Figure 2.6 shows the resonant frequency band of the simulated vibration data when ESR is applied. The results in Fig. 2.6 show that the ESR provides more detailed spectral contents for fault diagnosis though the device still samples the data at 50samples/sec. In Fig. 2.6, the horizontal difference between any two spectral spikes (marked points) is equal to the bearing defect frequency. Whereas, Fig. 4a has too poor resolution that it does not provide any details about the bearing fault. Comparing Figs. 2.5 and 2.6, it shows that it is easier to capture the mechanical faults using ESR with more detailed spectrum in terms of an increased resolution.
6. Iqbal Gondal, Muhammad Farrukh Yaqub, Xueliang Hua; Smart phone based machine condition monitoring system
This paper proposes a smart-phone based condition monitoring system. The advancement in the smart-phone technology in terms of computational complexity and the availability of the sensor-bank makes it easier to realize such a system. In this paper, digitized vibration data are captured using built-in accelerometer of a smart-phone and decomposed using wavelet packet transform (WPT). This is achieved by passing the digitized vibration through the appropriate low pass and high pass filters and then down sampled to decompose the signal into multiple frequency nodes or bands for multi-resolution analysis. The RMS values of wavelet decomposition nodes are computed for feature extraction or selection. It has been investigated that only a small portion of the overall vibration spectrum contains dominant information regarding the fault induced vibrations that is the resonant frequency band and the rest is noise. Hence a criterion was proposed to select the nodes containing relatively large signal energy and the corresponding node indices gives feature vector for residual life prediction model. The baseline data which represents the historical data is used to build the fault diagnostic model. The dominant features from the historical data are used to classify the nature of fault. The classifier built using the extracted feature is used for fault diagnosis and prognosis of unknown fault.
3. VIBRATION ANALYSIS
This project report is mainly concerned with the Vibration Analysis monitoring technique for the detection and diagnosis of faults pertaining to bearings.
3.1 Introduction to Vibrations
Vibration is a mechanical phenomenon whereby oscillations occur about an equilibrium point. The oscillations may be periodic, such as the motion of a pendulum. The vibrations produced are further classified based on their means of origin.
Fig 3.1 Types of Vibration
Free vibration occurs when a mechanical system is set in motion with an initial input and allowed to vibrate freely. There are no external disturbances other than the initial thrust provided to set the body in motion. Examples of this type of vibration are pulling a child back on a swing and letting it go, or hitting a tuning fork and letting it ring. The mechanical system vibrates at one or more of its natural frequencies.
Forced vibration is when a disturbance (load, displacement or velocity) is applied to a mechanical system. The disturbance can be a periodic or a transient input. The periodic input can be a harmonic or a non-harmonic disturbance. Examples of these types of vibration include transportation vibration caused by an engine or uneven road, or the vibration of a building during an earthquake. The body does not vibrate at its natural frequency instead is under the influence of an induced set of frequencies.
Damped vibration: When the energy of a vibrating system is gradually dissipated by friction and other resistances, the vibrations are said to be damped. The vibrations gradually reduce or change in frequency or intensity or cease and the system rests in its equilibrium position. An example of this type of vibration is the vehicular suspension dampened by the shock absorber.
Fig 3.2 Damped VibrationVibrational analysis (VA) deals with the transmission of highly complex vibrational signals from the rotating shafts comprising of large number of data. It is used to detect the characteristic changes of the shafts. After the detection of pattern variations owing to the presence of faults, the next stage is feature extraction from this data which consists of proper processing of the vibrational signal. The data extracted from the signals must contain highly reliable information regarding the faults indicated. The extracted information is then classified and fed into various systems, avoiding human tolerances, to propose a suitable diagnostic model. The vibrations produced during machine operation are mainly caused due to the effects pertaining to: shaft misalignment, bearing faults and crack formation. During the operation of rotatory machinery, the most common faults in bearings are witnessed due to the forces generated owing to the imbalanced nature of the machine. Various parameters that influence the working efficiency of the bearings, include uneven loading, improper tolerance limits used as standards during the installation and maintenance of the rotating machine. Insufficient periodic lubrication or the usages of lubricants that do not meet the required standards have a considerable effect on the bearings. The improper manufacturing techniques play a pivotal role in the occurrence of faults. Most commonly VA is used to detect faults in rotating equipment (Fans, Motors, Pumps, and Gearboxes etc.) such as Unbalance, Misalignment, rolling element bearing faults. VA can use the units of Displacement, Velocity and Acceleration displayed as a Time Waveform (TWF), but most commonly the spectrum is used, derived from a Fast Fourier Transform of the TWF. The vibration spectrum provides important frequency information that can pinpoint the faulty component.
In the time domain, the signal or function’s value is known for all real numbers, for the case of continuous time, or at various separate instants in the case of discrete time. An oscilloscope is a tool commonly used to visualize real-world signals in the time domain. A time-domain graph shows how a signal changes with time, whereas a frequency-domain graph shows how much of the signal lies within each given frequency band over a range of frequencies.
Fig 3.3 Time and Frequency Domain
The frequency domain refers to the analysis of signals with respect to frequency, rather than time. A time-domain graph shows how a signal changes over time, whereas a frequency-domain graph shows how much of the signal lies within each given frequency band over a range of frequencies. A frequency-domain representation can also include information on the phase shift that must be applied to each sinusoid in order to be able to recombine the frequency components to recover the original time signal.
A given function or signal can be converted between the time and frequency domains with a pair of mathematical operators called transforms. An example is the Fourier transform, which converts the time function into a sum or integral of sine waves of different frequencies, each of which represents a frequency component. The ‘spectrum’ of frequency components is the frequency-domain representation of the signal. The inverse Fourier transform converts the frequency-domain function back to the time function. A spectrum analyzer is the tool commonly used to visualize real-world signals in the frequency domain.
A fast Fourier transform (FFT) is an algorithm that samples a signal over a period of time (or space) and divides it into its frequency components. Using algorithms the time domain functions can be converted and represented in the form of frequency sine waves. These components are single sinusoidal oscillations at distinct frequencies each with their own amplitude and phase.
Fig 3.4 Fast Fourier Transform
A Time Domain graph is mainly used to represent the changes in the signal characteristic with respect to time. By far the most commonly used FFT is the Cooley–Tukey algorithm. This is a divide and conquer algorithm that recursively breaks down a DFT of any composite size N = N1N2 into many smaller DFTs of sizes N1 and N2, along with O(N) multiplications by complex roots of unity traditionally called twiddle factors. The Divide and Conquer algorithm mainly focuses on the multi-branch method of approach for recursive problems. These algorithms involves a continuous break down of the problems into simpler ones, one or more sub problems of the same relatable type until they can be solved easily. The individual solutions are then combined in order to obtain the complete solution of the original problem.
A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero. It can typically be visualized as a “brief oscillation” like one recorded by a seismograph or heart monitor. Generally, wavelets are intentionally crafted to have specific properties that make them useful for signal processing. Using a “reverse, shift, multiply and integrate” technique called convolution, wavelets can be combined with known portions of a damaged signal to extract information from the unknown portions.
As a mathematical tool, wavelets can be used to extract information from many different kinds of data, including – but certainly not limited to – audio signals and images. Sets of wavelets are generally needed to analyse data fully. A set of “complementary” wavelets will decompose data without gaps or overlap so that the decomposition process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet based compression/decompression algorithms where it is desirable to recover the original information with minimal loss.
Bearings over a period of time are subjected to wear. It is really important to be aware and to understand the different defects and faults they are prone to. These irregularities uusually tend to lower the operational period of the bearing and bring changes in its characteristics to transmit rotational energy. The faults affect different regions on the bearings and as a result are categorised into 4 different types
Inner Face Defects
Outer Face Defects
Rust is a film of oxide, hydroxide, or carbonate produced on a metallic surface by chemical action. Corrosion is the phenomena of oxidation or dissolution occurring on the surface and is produced by chemical action (electric chemical action including combination or cell restructuring with acid or alkali.
Fig 4.1 Effects on bearing due to rust
4.1.1 Causes for the occurrence of Rust
When equipment is stopped and its temperature decreases to the dew point, humidity in the housing turns into drops of water. The water drops often contaminate the lubricant. As a result, rust is generated on the bearing surface.
When bearings are stored in a humid place for a long time, rust is generated on the raceway surface at intervals equal to the rolling elements spacing.
Corrosion occurs when a sulfur or chlorine compound contained in lubricant additives decomposes under high temperature.
Corrosion occurs when water gets inside bearings.
4.2 Cage Defects
Cage is the outer casing of the balls. It is provided in order to prevent contamination of the lubricants from foreign elements and to keep the balls secure and free from wear and damage. The main reasons for these defects are:
Cracks and Chips- If a seriously cracked bearing is used under heavy operating conditions, it will fail.
Flaw and Distortion- Since cages are made from soft material, they tend to be damaged or become distorted by external forces or from contact with other parts. ?Since cages with a serious flaw also have distortion, their accuracy may decrease. And the motion of the rolling element is consequently affected; therefore, especially the size and location of the flaw should be checked with care.
Rust and Corrosion- If rust or corrosion is found on cages, it can be assumed that it is also occurring on the bearing ring and rolling element.
Looseness and Improper Riveting Looseness of the rivet is caused by an error in bearing mounting, moment load, variable load, vibration, etc. If a bearing is operated with improper riveting, the bearing cannot be returned to service because the rivets may break
Fig 4.2 Cage defects
1. Cracks and Chips
Abnormal load, Vibration impact.
2. Flaw, Distortion
Improvement of sealing capability. Periodic inspection of lubricant.
Provision of adequate rust prevention during storage of bearings
Improper lubricant or shortage of lubricant.
Contamination by foreign matter … Improvement of sealing capability.
Looseness and Cut-Off of Rivet
Improper bearing mounting.
Reduction of bearing inclination.
Severe load or vibration
Pear skin is a condition of the rolling surface where small depressions are created entirely as a result of many foreign matters being caught between parts. A rolling surface suffering from pear skin appears dim and is rough in texture. In the worst case, the surface is discolored by heat.
Discoloration is a phenomena in which the bearing surface is discolored by staining or heat generated during operation.
Fig 4.3 Defects due to pear skin
Causes of Pear skin
Since pear skin is mainly caused by contamination by foreign matter or lack of lubricant, these two points should be inspected most carefully.
Discoloration (staining) is caused by deterioration of the lubricant or adhesion of colored substances to the bearing surface. Some of these substances can be removed by scrubbing or wiping with a solvent.
A brown discoloration of the rolling or sliding surface is caused by adhesion of acidic powders generated by abrasion during operation. In general, these powders adhere uniformly to the bearing circumference.
In order to classify the type of defect occurring in the bearing, it is important to tabulate the specifications of the bearing and calculate the dominant frequencies and classify whether the fault is pertaining to the Inner Face, Outer Face, Balls (Rolling Element) or the Cage.
4.4 Wear and Fretting
Wear is caused mainly by sliding abrasion on parts including the roller end face and rib, cage pocket surface, cage, and the guide surface of the bearing ring. Wear due to contamination by foreign matter and corrosion occurs not only to the sliding surface but also to the rolling surface.
Fig 4.4 Defects due to bearing Wear
Fretting is a phenomena which occurs when slight sliding is repeatedly caused on the contact surface. On the fitting surface, fretting corrosion occurs, generating a rust like powder. If bearings receive a vibration load when they stop or operate, slight sliding occurs in the section between the rolling element and bearing ring due to elastic distortion. False brinelling, a flaw similar to brinelling, is generated by this condition.
Fig 4.5 Fretting
1) Improper lubricant or shortage of lubricant.
2) Contamination by foreign matter.
2?Slight vibration on fitting surface caused by load
VibSensor makes collecting, analyzing, and exporting high quality accelerometer data easy. Read on to learn about the four main functions of the app, followed by a discussion of getting the best data from your device.
Fig 5.1 VibSensor Application Logo
Mobile devices contain an accelerometer that measures along each of the main axes of the device. By convention, the axes are labeled as follows.
Fig 5.2 Accelerometer axes
The raw accelerometer data contains the effects of gravity plus any other accelerations the device may be experiencing. In Live View, this data is split into slowly varying Tilt and quickly varying Vibration data. A few minutes of play with Live View will teach you how this data relates to the orientation and vibration of your device.
Fig 5.3 Tilt along the axes
For tilt data, a full hemisphere means one g of acceleration. The scale for vibration data is indicated by the small scale bar, and ranges from 0 to 0.5g. Typical devices actually have a full range of -2g to +2g. The smaller range in Live View is chosen to emphasize smaller vibrations.
The Acquire page allows timed or vibration activated acquisition of accelerometer data. Select the acquisition type on the Settings page.
Fig 5.4 Methods for Data Acquisition
Input a Title for the collection, set the Delay and Duration, then press Start. It is possible to Pause or Stop an ongoing collection. Pausing introduces time gaps in the data.
Vibration Activated Acquisition
Data collection is triggered by an event where the net acceleration exceeds the activation level in a one second interval.
Fig 5.5 Vibration Activated Data Acquisition
Up to ten seconds of data before the event are also recorded. To set up the collection, first set the activation level. A red “Triggered” indicator is displayed whenever the activation level is exceeded. This will help you choose the best level for triggering. Similar to the timed acquisition, input a Title for the collection, set the Delay and Duration, then press Start. It is possible to Pause or Stop an ongoing collection. Pausing introduces time gaps in the data. Once a collection is started, it remains in the armed state until the collection is triggered. Multiple collections can be captured by setting Max Recordings.
Each data acquisition consists of raw, time-stamped acceleration data that is automatically saved to a database with a date and time stamp that allows later viewing, analysis, and export. So acquire as much as you like, then analyze and export later.
The Acquire page allows timed acquisition of accelerometer data. Set the Delay and Duration of the collection, then press Start. It is possible to Pause or Stop an ongoing collection. Pausing introduces time gaps in the data.
Fig 5.6 Data Acquisition for High Frequency
Each data acquisition consists of raw, time-stamped acceleration data that is automatically saved to a database with a date and time stamp that allows later viewing, analysis, and export. So acquire as much as you like, then analyze and export later.
Fig 5.7 Defined Settings prior to Acquisition
The frequency range of acquisition can be modified in the settings. The “high” range collects at the maximum rate supported by the device, and allows analyzing vibrational frequencies between 0.03 and 50 Hz. The “low” range extends this by averaging to allow analysis of vibrations with periods as long as 5 minutes.
The View Data page allows access to all data collected with your device. The data is segmented first by day, then collection time. Select the data of interest to view the analysis page. There are two main elements to the analysis page:
The report contains a summary of the data collection. Pay attention to the Gaps and Data Rate information – it will let you know if your device is acquiring up to its capability. The acceleration units can be changed in the main Settings page.
This indicates the maximum acceleration data recorded for each axis. LIMIT means that data occurred that was outside of the measurement range of the accelerometer.
ISD (Integrated Spectral Density)
The power spectral density integrated from 0 Hz to the Nyquist frequency. For a constant vibration level, this should be equal to the RMS vibration level.
These are identified from the calculated power spectral density. The top two resonances are shown for each axis, with the magnitude of each indicated in parenthesis next to the resonance frequency.
This data is displayed in the report after the vibration data is viewed in the plot. It is the root-mean-square amplitude of the vibration data about zero.
4.3.6 Average tilt
This data is displayed in the report after the tilt data is viewed in the plot. It is the average of the tilt data over the duration of the acquisition.
The plot allows interactive viewing of four types of data. The data type can be selected along the top of the plot. At the bottom of the plot is an interactive legend. Touch a legend entry to toggle that trace on or off. Also on the bottom is an x-axis scale factor. Touch to select the desired scale factor, then swipe to scroll through the data. The four types of data are:
Power spectrum: The power spectrum is calculated from 0 Hz to the Nyquist frequency. The units are acceleration squared divided by the frequency. To integrate over the power spectrum, sum all data and then multiply by the frequency step size. This returns the mean squared amplitude in acceleration units squared. Both x and y axes can be toggled between linear and log by selecting the desired label next to the axis.
Vibration: The raw accelerometer data high-pass filtered to emphasize vibration. The roll-off frequency is 1 Hz in high frequency mode, and 0.1 Hz in low frequency mode.
Tilt: The raw accelerometer data low-pass filtered to emphasize tilt. The roll-off frequency is the same as for vibration.
Raw: The unmodified accelerometer data from the device.
Exporting data is straightforward. Navigate to the data display, select the type of data to export on the plot, then select the upload button on the task bar. The export format and whether or not to include the report can be selected on the main Settings page.
5.6 Data Quality
To ensure highest data rate, VibSensor is designed to only run in the foreground. If a data collection is in progress and VibSensor is run in the background, the data collection will pause until VibSensor is in the foreground again. Make sure your device is set to remain on (for example, auto-lock disabled), and don’t run other applications in the background.
Verify that you are achieving the expected data rate by inspecting the report for each acquisition. If you notice gaps in the data, or the data collection rate is highly variable from acquisition to acquisition, make sure no other applications are running in the background.
5.7 Artifacts: In repeated tests, VibSensor has proven reliable and accurate for different device types. However, there are three things that can affect data quality that every user should be aware of:
Saturation: If you acquire vibration data that is consistently at the accelerometer limit, you will not have accurate data. Clipping and artifacts in the power spectrum will appear. Note that if one axis already has one g of tilt, it lowers the saturation level for that axis. However, the other axes can be used even if one is in saturation.
Aliasing: If you acquire data with strong vibrational resonances above the Nyquist frequency, they will be aliased to lower frequencies. Acquiring data with different mobile devices that have different Nyquist frequencies can help uncover this effect.
Sympathetic vibrations: For the best data, make sure your mobile device is firmly resting on the surface to be analyzed. If the mobile device is bouncing or rocking, then the true acceleration is not being measured, but also the motion of the mobile device
An accelerometer is a device that measures proper acceleration. Proper acceleration, being the acceleration (or rate of change of velocity) of a body in its own instantaneous rest frame, is not the same as coordinate acceleration, being the acceleration in a fixed coordinate system. For example, an accelerometer at rest on the surface of the Earth will measure an acceleration due to Earth’s gravity, straight upwards (by definition) of g = 9.81 m/s2.
Accelerometers have multiple applications in industry and science. Highly sensitive accelerometers are components of inertial navigation systems for aircraft and missiles. Accelerometers are used to detect and monitor vibration in rotating machinery. Accelerometers are used in tablet computers and digital cameras so that images on screens are always displayed upright.
Fig 6.1 In-built Smartphone Accelerometer
Single- and multi-axis models of accelerometer are available to detect magnitude and direction of the proper acceleration, as a vector quantity, and can be used to sense orientation (because direction of weight changes), coordinate acceleration, vibration, shock, and falling in a resistive medium (a case where the proper acceleration changes, since it starts at zero, then increases).
Fig 6.2 Axes defined in the Accelerometer
Accelerometers are also used for machinery health monitoring to report the vibration and its changes in time of shafts at the bearings of rotating equipment such as turbines, pumps, fans, compressors or bearing fault which, if not attended to promptly, can lead to costly repairs. Accelerometer vibration data allows the user to monitor machines and detect these faults before the rotating equipment fails completely.
Sensitivity of the accelerometer, sometimes referred to as the “scale factor” of the accelerometer, is the ratio of the sensor’s electrical output to mechanical input. Note that a transducer is defined generally as a device that converts one form of energy to another. An accelerometer is simply a transducer that converts mechanical acceleration into a proportional electrical signal. Typically rated in terms of mV/g or pC/g, it is valid only at one frequency, conventionally at 100 Hz. Since most accelerometers are influenced to some degree by temperature, sensitivity is also valid only over a narrow temperature range, typically 25 ±5°C. Additionally it is valid only at a certain acceleration amplitude, usually 5 g or 10 g. Sensitivity is sometimes specified with a tolerance, usually ±5% or ±10%. This assures the user the accelerometer’s sensitivity will be within this stated tolerance deviation from the stated nominal sensitivity. In almost all cases, accelerometers are supplied with a calibration certificate stating its exact sensitivity (within measurement uncertainty limits). Sensitivity is called the “reference sensitivity” when referring to the percentage or dB tolerance band of frequency response specifications. See frequency response below. Sensitivity is called the “axial sensitivity” when discussing transverse sensitivity. See transverse sensitivity below. Despite the tight constraints that surround the sensitivity specification, this is the number that is most frequently used for programming a signal conditioner or data acquisition system. A signal conditioner and/or DAQ system uses this number to process and interpret the signal from the accelerometer.
Mounted resonant frequency is the point in frequency in the accelerometer’s frequency response where the accelerometer outputs maximum sensitivity. It is specified in units of hertz (Hz). Typical accelerometers exhibit a mounted resonant frequency above 20 kHz, although some show as high as 90 kHz. As the name implies, it is the result of the natural resonance of the mechanical structure of the accelerometer itself. Certainly if the resonance of the accelerometer were measured in “free space” it would be different than if mounted to a structure. However, this is an impractical application for a piezoelectric accelerometer, thus the designation “mounted” is added. It is not a design goal of manufacturers to produce an accelerometer that has a mounted resonant frequency within a certain tolerance. Instead, mounted resonant frequency is specified as a minimum, ensuring to the user that this resonant point will not occur below the minimum. As such, mounted resonant frequency is a rough “figure-of-merit” that sets the upper limit of the frequency bandwidth of the accelerometer. For piezoelectric accelerometers, whose mechanical structure is almost completely undamped, the amplitude of the resonant peak can be quite high, resulting in a sensitivity many times higher than the specified reference sensitivity. As such, any vibration at or near the frequency of the resonant peak will be highly amplified, resulting in distorted measurements and corrupted data. A design goal of manufacturers, then, is to push the mounted resonant frequency point as high as possible in the accelerometer’s structure, with the intent that the point be well beyond any vibration frequencies in the user’s measurement application. The user also has to ensure that no vibration frequency components are at or near the mounted resonant frequency point. Note that mounted resonant frequency is specified assuming ideal accelerometer mounting conditions. Just as the manufacturer can influence the mounted resonant frequency point with the accelerometer’s mechanical structure itself, so too can external structural factors (which the user controls). As mechanical resonance characteristics in general are dependent on material stiffness and damping, it is critical the accelerometer be mounted correctly and as stiff as possible. Improper mounting generally decreases stiffness and increases damping, causing the resonant peak to decrease in frequency and the width of the resonant rise to increase (i.e. mechanical Q is lowered). The ultimate result of this will, if allowed to degrade enough, affect the frequency response of the accelerometer.
This project provides the application of an In-built Smartphone Accelerometer.
The accelerometer is a new technology which has upgraded the user’s experience in touch screen smart devices like mobiles and tablet PCs. The main function of this is to adapt the orientation change when the position is changed from vertical to horizontal and vice-versa. When the screen is tilted horizontally for landscape view, the function of smartphone accelerometer is to sense this change and adapt the screen accordingly. Along with this, this feature helps developer to make games and other apps user friendly. So, in short, smartphone accelerometer is a device to gauge or measure the phone/tablet’s orientation – tilt or landscape or portrait. Hence we can say that this is one of the coolest feature in any smartphone.
The smartphone accelerometer contains circuit having ‘Seismic mass’ (made up of Silicon) and it goes on changing its position according to the orientation change and ‘housing’ attached to the device’s circuit.
Actually a smart device with accelerometer is nothing but a circuit based on MEMS (Micro Electro Mechanical System), that senses or measures the forces of acceleration that may be caused due to gravity of movement or tilting action. So, it is a device to measure the speed of acceleration or movement to which it is attached. If it is employed in mobile, it will do accordingly. It also senses the angle at which it is being held via mobile.
Fig. 6.3(a&b) Working of Accelerometer
The figure 6.3(b), a cutout of figure 6.3 (a), shows change in capacitance as a result of change in position of Seismic mass, when smart device is tilted or changed in orientation. This will recognize the change in gravitational pull by changing the current equivalent to capacitance change. This is the working principle of smartphone accelerometer.
Power Spectrum Density of a Signal
In vibration analysis the PSD stands for the Power Spectral Density of a signal. Each word is chosen to represent an essential component of the PSD.
Power refers to the fact that the magnitude of the PSD is the mean-square value of the signal being analysed. It does not refer to the physical quantity power (as in watts or horsepower). But since power is proportional to the mean-square value of some quantity (such as the square of current or voltage in an electrical circuit), the mean-square value of any quantity has become known as the power of that quantity.
Spectral refers to the fact that the PSD is a function of frequency. The PSD represents the distribution of a signal over a spectrum of frequencies just like a rainbow represents the distribution of light over a spectrum of wavelengths (or colours).
Density refers to the fact that the magnitude of the PSD is normalized to a single hertz bandwidth. For example, with a signal measuring acceleration in units of G’s, the PSD has units of G2/Hz.
The mean-square value (power) is a convenient measure of the strength of a signal. This is illustrated in Fig. 7.1 which shows the time history of the vibration of the floor panel of a car traveling on the highway as measured by an accelerometer. The average amplitude of the signal cannot be specified by the mean value since this is near zero. Instead the signal is squared (resulting in a positive quantity) and then the mean value is computed. To obtain a linear value (in G’s for this case) the square root is taken to obtain the RMS (root-mean-square) value.
Figure 7.1 Vibration of Car Floor Panel, Mean-Square Value = 0.0053 G2, RMS value = 0.073 G
The mean-square value must be used when combining signals of different frequencies. This is illustrated in Fig. 7.2 where two sine waves of different frequencies are added together. The mean-square value of a unit sine wave is 0.5 and the RMS value is 0.707. When the two are added the mean-square value is 1.0 and the RMS value is 1.0.
Figure 7.2 The Mean-Square of (A+B) Equals the Sum of the Mean-Squares of A and B (With the Mean Values of A and B Being Zero)
Figure 7.3 Illustration of the Mean Value of 2AB
The frequency distribution of a signal is very useful information when dealing with systems having resonances. This is illustrated in Fig. 7.4 where a cantilever beam is being driven at the base by a broad band signal (having a wide distribution of frequencies) and an accelerometer is measuring the tip vibration. It is difficult to determine from the time history of the signal the values of the resonance frequencies of the beam. However, the peaks in the frequency spectrum of the tip vibration clearly show the resonance frequencies.
Figure 7.4 Time History and Frequency Spectrum of Beam Vibration Signals
The magnitude of the frequency distribution of a signal depends on the number of frequency bands in the distribution. This is illustrated in Fig. 7.5 where the frequency spectrum of the car vibration signal is computed with three different frequency bandwidths. The squared magnitudes of the spectra are proportional to the frequency bandwidth. To overcome this variation, the PSD divides the squared magnitude by the frequency bandwidth to give a consistent value independent of the band width used.
Figure 7.5 Dependence of the Frequency Spectrum on the Frequency Bandwidth
41167054155428393134257380904418367900 2 3 6
3475688135191122 22 4 15459281973662005532449163816302364831739833
3 5 7
Fig 8.1 Experimental Set-up
Aluminium Alloy(H9) Shaft
Wooden Base Plate
12v DC Motor
8.1 Aluminium Alloy 6063(H9)
6063 Aluminum is a 6000-series aluminium alloy .AA 6063 is an aluminium, with magnesium and silicon as the alloying elements. The standard controlling its composition is maintained by The Aluminium Association. It has generally good mechanical properties and is heat treatable and weld able. It is similar to the British aluminium alloy H9.
Table 8.1 Element Composition in Aluminium Alloy (H9)
Element Percentage present
Si 0.2 to 0.6
Fe 0.0 to 0.35
Cu 0.0 to 0.1
Mn0.0 to 0.1
Mg 0.45 to 0.9
Zn 0.0 to 0.1
Ti 0.0 to 0.1
Cr 0.1 max
6063 aluminium is a 6000-series aluminium alloy: there is significant alloying with both magnesium and silicon, and the alloy is formulated for primary forming into wrought products. 6063 is the Aluminium Association (AA) designation for this material. In European standards, it will typically be given as EN AW-6063. H9 is the British Standard (BS) designation. A96063 is the UNS number. It has been in use since 1944, but has only received its standard designation in 1954. This material is well established. The Further Reading section below cites a number of published standards, and that list is not necessarily exhaustive. It can have the lowest tensile strength relative to other 6000-series alloys in the database. The properties of 6063 aluminium include thirteen common variations.
Table 8.2 Properties of Aluminium Alloy (H9)
Density (?)2.69 g/cm
Young’s modulus (E)68.3 GPa (9,910 ksi)
Tensile strength (?t) 145–186 MPa (21.0–27.0 ksi)
Elongation (?) at break18-33%
Poisson’s ratio (?)0.3
Melting temperature (Tm)615 °C (1,139 °F)
Thermal conductivity (k)201-218 W/m*K
Linear thermal expansion coefficient (?)2.34*10?5 K?1
Specific heat capacity (c)900 J/kg*K
Volume resistivity (?)30-35 nOhm*m
8.2 Ball Bearing
A bearing is a machine element that constrains relative motion to only the desired motion, and reduces friction between moving parts. The design of the bearing may, for example, provide for free linear movement of the moving part or for free rotation around a fixed axis; or, it may prevent a motion by controlling the vectors of normal forces that bear on the moving parts. Most bearings facilitate the desired motion by minimizing friction. Bearings are classified broadly according to the type of operation, the motions allowed, or to the directions of the loads (forces) applied to the parts.
Fig 8.2 Ball Bearing
The shaft is supported by two bearings one at the drive end and the other at the non-drive end which is free. The bearings used are 6201ZZ shielded ball bearing having dimensions 12*32*10.
Fig 8.3 6201-ZZ Radial Ball Bearing 12X32X10
The 6201-ZZ ball bearing has two non-contact metal shields one on each side of the ball bearing. The 6201-ZZ is a 12mm ball bearing that is found in many applications. These bearings are engineered for use in high-speed, high precision applications for agriculture, automotive, chemical, general industrial utilities
Table 8.3 Ball bearing Specifications.
Technical Specifications Inside Diameter 12mm
Outside Diameter 32mm
Ball Bearing type Deep Groove
Race width 10mm
End type Shielded
Bore Type Parallel
Cage Material Steel
Pitch Diameter (Pd) 22mm
Ball Diameter (Bd) 5.95mm
No. of Rolling Elements 7
Contact Angle(?) 0
Static Load rating 3.05kN
Dynamic Load Rating 6.8kN
Maximum Speed (Grease) 22000rpm
Operating Temperature -10 to +110 ?C
One end of the shaft at the drive end is connected to a DC motor through a flexible coupling. The coupling used was a flexible beam coupling made of aluminum having bore diameters of 6mm (motor shaft) and 12mm (aluminum shaft).
Fig 8.4 Flexible Beam Coupling
This coupling was chosen as it is designed to operate at higher speed, greater misalignment (3 degree angular and 0.2mm parallel) and limited lubrication further each coupling is manufactured as one piece, thereby eliminating mechanical joints and possible failure points. The motor has a capacity of 12 volts and 3-4 amps which it draws from a battery eliminator. The eliminator converts the 220 volts AC supply from the ordinary plug point to DC voltage in incremental steps of 2 volts from 2 to 12 volts at 1-4 amps in order to vary the voltage and hence the speed of the motor. A non-contact digital photo laser tachometer was used to measure the speed of the shaft. The motor and the bearing were held in position on a wooden block using claps.
8.3 Battery Eliminator
A battery eliminator is a device powered by an electrical source other than a battery, which then converts the source to a suitable DC voltage that may be used by a second device designed to be powered by batteries. A battery eliminator eliminates the need to replace batteries but may remove the advantage of portability. A battery eliminator is also effective in replacing obsolete battery designs. A battery eliminator is any device which provides power to a circuit, replacing the need or use of batteries.
Fig 5.5 Battery Eliminator
A battery eliminator, just as the name states, eliminates the need to use batteries to power a circuit. It is any device which provides power to a circuit, replacing batteries.
We’ll now go over each part of this circuit and go over the role each component plays, so that you can know how this circuit works in its entirety
Fig 8.6 Circuit diagram of a Battery Eliminator
AC Plug- The first part of the circuit is the AC plug. When we create a battery eliminator, it creates DC voltage from the AC mains voltage from a wall outlet. To build a battery eliminator, purchase a 3-prong AC plug. It can also work with a two-prong AC plug. But having a 3-prong plug is better because ground provides better against possible electric fires.
Transformer- after the AC plug, we need a step-down transformer. The transformer’s job is to take the 120V AC voltage from the mains line and step it down to 15-18 volts. This is because our battery eliminator will supply variable DC voltage of 1-20V. Therefore, we lower the very high voltage that we get from the mains outlet from the wall into a smaller voltage. It must still exceed the voltage of the DC which we want to output. Since we want to create up to 20VDC variable voltage output, we need a transformer that converts the mains voltage to a voltage that is higher than this 20V. A step-down transformer is a great device for lowering voltage from a mains AC voltage line.
Full-wave Rectifier- The next component we need in our circuit is a full-wave rectifier. The job of the full-wave rectifier is to take the AC voltage from the transformer and rectify it so that the voltage no longer goes through a negative cycle. With the rectifier, all of the voltage is rectified positive.
Fig 8.7 Full-Wave Rectifier Bridge
You can see how all voltage waveforms are now above the positive line. This is called pulsating DC voltage. We will later add more components so that this can be a nearly perfectly smooth DC output, which is what is desired.
Smoothing Capacitor- The next component after the full-wave rectifier is the smoothing capacitor. The smoothing capacitor acts to smooth out fluctuations in a signal, so that there is less fluctuation. As you saw in the previous component, the rectifier creates pulsating DC signals. The smoothing capacitor, now, a 2200?F capacitor, acts to even out this pulsating fluctuations to create a smoother waveform.
Fig 8.8 Smoothing Capacitor
This regulator serves a twofold purpose. First, it serves to further smooth out the fluctuating signal, so that it’s a perfectly smoothing DC signal.
A regulator is a device that “regulates” voltage so that a perfectly smoothing DC voltage is output.
Fig 8.9 Voltage Regulator
The second purpose of the regulator, since it is an adjustable voltage regulator, is to produce variable DC voltage as output. How we vary voltage is by flipping the rotary switch to the voltage we desire outputted.
One reason is that can be a substitute if you do not have batteries, or you do not have the correct type, or if all of your batteries have died. Being that most electronic circuits run off of DC power, you can use any DC power source to power the electronic device, as long as you use the voltage it needs.
Heat sink- One thing we must do to the voltage regulator is attach a heat sink to it. This is vital for this application.
This is because when we use a regulator, we input a voltage into it and it outputs the voltage, based on the values of resistor R1 and the resistance value which the rotary switch is connected to. When the rotary switch is connected to its highest resistance, it doesn’t dissipate that much heat. Since our transformer outputs 15-18V, when the rotary is set to 2.6K?, the regulator outputs 12V. 15-12V=3V. Thus, not that much wasted voltage is created. However, if the potentiometer is set to 48?, the regulator outputs approximately 1.5. 15V-1.5V= 13.5V of wasted, dissipated energy. This creates a lot of heat, since the voltage difference between input and output voltage is so great. Any difference appears as heat. So the greater the difference, the greater the heat. This is the r reason it is vital to attach a heat sink to the regulator. When the difference between input and output voltage is great, it appears as heat. We must have a way to dissipate this heat, or else it can damage or destroy the circuitry of the battery eliminator. The way to do this is to use a heat sink.
Fig 8.10 Heat Sink
The test rig consists of a shaft made of aluminum alloy (ISS H9 or Equivalent alloy AA U.S.A. 6063) connected to a 12V DC motor through a flexible coupling. A battery eliminator is used to convert the 220-230V AC supply down to 2-12V DC supply. The battery eliminator is used to change the voltage in order to obtain varying operating speed of the shaft. The shaft is supported by two bearings one at the drive end and the other at the non-drive end which is free. The entire set-up is placed on top of a wooden block, in order to facilitate the damping of the vibrations produced during operation. The test is performed on the bearings to identify, detect and diagnose the faults, if any. A smartphone is used and placed atop of the bearing, under experimentation. It is used to capture the vibrational signals produced during operation. The data is acquired using the Timed Acquisition Method for high frequencies for a period of sixty seconds. Signals were recorded along the radial and axial directions at 900 and 1500 rpm. The set-up was operated using a fault-free bearing in order to detect and diagnose the faults occurring in the machine due to Angular/ Parallel Misalignment, Mass Imbalance and Bent Shaft. Following this, the fault-free bearing was replaced by a faulty bearing at the non-drive end to obtain the second set of readings. The signals are then processed using the Fast Fourier Transform algorithm to obtain the feature characteristics with respect to the frequency in the form of Power Spectrum Density (PSD). The PSD is then studied to identify the defects and understand the characteristic of the bearings.
Fig 9.1 Representation of Condition Monitoring Method
Results and Discussion
Table 10.1 Initial Readings
Serial No. Input Voltage (V)
Output Voltage (V) Ampere (A) RPM Frequency (Hz)
1. 2 3.21 1.2 900 15
2. 4 6.15 1.83 1500 25
3. 6 7.48 2.05 2700 45
1 Revolution per minute is equal to 1/60 Hertz. RPM is commonly used to measure engine performance. Period is the inverse of frequency. 1 RPM = 1/60 Hz or approximately 0.0166666666666667 Hz.The knowledge base was constructed to detect the faults associated with mechanical systems including roller bearings, gears, belt drives, couplings, shafts, structural vibrations. Maintenance Knowledge Base is developed from published literature on maintenance management like handbooks, journals and conference proceedings is as shown in Table 4.2:
Table 10.2 Fault Diagnostic Chart
To identify the type of the bearing characteristics frequency, the cause of the defect has be determined. The bearing frequency multipliers equations provide a theoretical estimate of the frequency to be expected when the bearing elements defect takes place. To calculate these frequency multipliers for the REB (Rolling Element Bearing) in which the inner race rotates and the outer race is stationary.
Fig 10.1 Formulas to calculate and identify type of bearing fault
Since the test rig was assembled using basic tools, there are certain errors and inaccuracies are present in it due to which there are slight shift in peaks as observed in the graphs.
RPM- 900 Frequency – 15Hz
Axis- RadialAxis- Axial
Fig 10.2 Spectrum indicating probability of Bent Shaft at 900 rpm
As the amplitude is high at 1x frequency i.e. at 15 Hz in radial and axial direction, then from the fault diagnostic chart the problem is identified as “bent shaft”.
RPM- 1500Frequency- 25Hz
Axis- Radial Axis- Axial
Fig 10.3 Spectrum indicating probability of bent shaft at 1500rpm
Similarly, the amplitude is high at 1x frequency i.e. at 25 Hz in radial and axial direction, then from the fault diagnostic chart the problem is identified as “bent shaft”.
Table 10.3 Bearing Parameters at 900 rpm
Ball Bearing :6201ZZ
Ball Diameter (Bd) 5.95mm
Pitch Diameter (Pd) 22mm
Contact Angle (?) 0
No. of Rolling Elements 7
BPFI (Hz) 66.6
BPFO (Hz) 38.295
BSF (Hz) 20.2305
FTF (Hz) 5.46
Fig 10.4 Spectrum indicating bearing faults at 900 rpm
As the peak is identified at 1x BPFO and the amplitude is slightly high, then from the fault diagnostic chart the problem is identified as “Outer Race Defect”.
Similarly a peak is identified close to 1x BSF and the amplitude is very high, then from the fault diagnostic chart the problem is identified as “Ball Defect”.
Similarly a peak is identified close to 1x FTF and the amplitude is slightly high, then from the fault diagnostic chart the problem is identified as “Cage Defect”.
The “Inner Race Defect” could not be detected as the maximum frequency of the graph obtained is limited to 50HZ.
Table 10.4 Bearing Parameters at 1500rpm
Ball Bearing :6201ZZ
Ball Diameter (Bd) 5.95mm
Pitch Diameter (Pd) 22mm
Contact Angle (?) 0
No. of Rolling Elements 7
BPFI (Hz) 111
BPFO (Hz) 63.825
BSF (Hz) 33.718
FTF (Hz) 9.1
Fig 10.5 Spectrum indicating bearing fault at 1500 rpm
As a peak is identified close to 1x BSF and the amplitude is high, then from the fault diagnostic chart the problem is identified as “Ball Defect”.
Similarly a peak is identified close to 1x FTF and the amplitude is slightly high, then from the fault diagnostic chart the problem is identified as “Cage Defect”.
The “Outer Race Defect” and “Inner Race Defect” is not detected as graphs is limited to a frequency of 50HZ due to device limitation.
This project focused on assisting the machine health analyses as well as making this technology available to a broader user base who are not experts in the condition monitoring field. This paper proposes a smart-phone based condition monitoring system. The advancement in the smart-phone technology in terms of computational complexity and the availability of the sensor-bank makes it easier to realize such a system. In this paper, vibration data are captured using built-in accelerometer and classification results are presented. Knowingly that the architecture of these phones is not robust enough to bear the industrial environment, it is expected that the robustness will achieved in future technologies. Despite of the device limitation in terms of the sampling rate. The enhancement in the sampling rate is achieved by capturing the samples after a certain precise delay between the revolutions. From this technique, we can satisfactorily distinguish between the type of faults in the bearing and validate it through the theoretical values.
The future works include the usage of smart-phone not only for localized fault diagnosis and prognosis but also extending it towards datalogging using wireless communication. Results obtained from the proposed system validate the prospects of extensive usage of smart-phones in the future condition monitoring services, and other related industrial application. With the advancement in the smartphone technology, the sampling rate can be enhanced for higher rpm’s. The acquired data can be stored on public servers and can be accessed to detect and diagnose the faults, enabling Online Condition Monitoring.
M. Mohamed Musthafa, S.P. Sivapirakasam, M. Udayakumar, “Comparative studies on fly ash coated low heat rejection diesel engine on performance and emission characteristics fueled by rice bran and pongamia methyl ester and their blend with diesel”, Energy 2011;36:2343-2351.
X. Sun, W.G. Wang, R.M. Bata, X. Gao, “Performance evaluation of low heat rejection engines”, ASME Transcations 1994; 116:758-54.
Bahattin Iscan, Huseyin Aydin, “Improving the usability of vegetable oils as a fuel in a low heat rejection diesel engine”, Fuel Processing Technology 2012;98:59-64.
B. Karthikeyan, K. Srithar, “Performance characteristics of a glowplug assisted low heat rejection diesel engine using ethanol”, Applied Energy 2011;88:323-329.