Bearing as the Main Part in the Majority of Machines - Coursework Example

 ABSTRACT Bearing is the most important component in the majority of machines and requirements are made upon their carrying capacity. Moving parts in machinery involve rolling motion or relative sliding. One of examples of rotation motion can be motor vehicle wheel. Most bearings are used to support rotating shafts in machines and generate energy losses by wear and friction. Bearings have spectacularly long service lives when maintained and applied properly. The most common rolling element bearing types are ball, spherical roller, tapered roller and cylindrical roller. Bearings do not show any signs of wear unless pollutants, such as abrasive external matter or dirt get into them. Bearings that are properly selected, correctly lubricated, and protected from abuse will usually outlast the machines in which they are installed The optimum duration bearing life is achieved when worsening due solely to rolling fatigue finally results in favorable operation. This aim of this essay is to assist in identifying causes of bearing failures and provide instruction or direction on how to keep away from future problems. Suitable bearing design can minimize wear and friction as well as early failure of machinery. The most important objectives of bearing designs are to achive long bearing life by reducing friction energy and wear, and lower maintenance operating cost and output of machinery by avoiding repeated bearing failures. In manufacturing plants, unforeseen bearing failures often cause expensive loss of production. If the machinery is plagued by frequent bearing problems, the explanation that complements the text can provide precious assistance in identifying the root cause of a bearing failure. In case of bearing failure, inspection and through cleaning is advisable, comparing your observations to the material in this series on bearing failure analysis. The first thing to look for is an explanation that describes similar damage to the case at hand. Lubrication, bearing materials, and the introduction of rolling-element bearings and bearings supported by lubrication films need special care. Perfection in bearing technology results in lower wear friction and maintenance expenses, as well as in the longer life of machinery. Keywords: bearing faulty, vibration analysis, condition monitoring . 1.INTRODACTION Conditional monitoring(CM) accepted by many industries and companies and long term users no longer bother to determine its cost and benefits; it is suggested that the assessment be be done yearly on a sampling basis to provide current evidence of the value of the approach to the business. This gives personal satisfaction. One tip of particular note for condition monitoring practitioner and also ensures continuation in the event of a change in management is to report their recommendations and findings clearly and precisely. They must leave the technical details and plant history file to the later parts of the report. Often, the specialists provide learned documentation, when the asset owner just wants to know if any maintenance action is needed. They need to formulate the possible implications if the recommendations are not followed and ensures maintenance in the event of a change in management. CM gives early detection of damage and wear outs in most cases. Also, small but potential faults are detected early when CM professionals tour the plant methodically. CM saves costs and reduce spare usage as well. CM plays an important role in improving product quality, customer relations, plant design and in turn the overall company efficiency and its sustained market presence. 3.1 Rolling element bearings A rolling element bearing comprises of outer and inner races, rolling elements and cage; a technical fault can occur in any part of the bearing and can be a reason for the high frequency. The gravity of the wear may change the exact vibration type. The rolling element and raceways defects are generally easier to detect. There are techniques and procedures available to detect the exact point of a technical defect, however, there are no established manuals to forecast when a bearing fault turns into a functional failure. In an earlier topic dealing with demodulation and enveloping, we see how bearing faults generate both the bearing fault frequency and the ringing random vibrations, which are the resonant frequencies of the bearing elements. 3.2 Stages of bearing defect 3.2.1 Stage 1 of bearing defect The FFT spectrum of bearing faults could be divided into three zones (A, B and C). We will make a note of changes as and how bearing wear moves along. The first significance of bearing wear shows up in the ultrasonic frequency series from nearly 20–60 kHz (120–360 kcpm). These frequencies are estimated by high frequency detection techniques, like Spike Energy SE, SEE, PeakVue and others. As Figure 3.10 shows that the rolling elements or raceways defects of the bearing can’t be observed during the first stage. The raceways might not be able to have the brilliance of a new bearing and may appear pale gray. (Figure 3.1 small fault in the raceways of a bearing) Figure 3.1 small defect in race way 3.2.2 Stage 2 of bearing defect In next stage (Figure 3.2), the fatigued raceways start developing minute pits(making hole in it). Rolling elements passing over these holes begin to generate the ringing or bearing element natural frequencies that often are in the 30–120 kcpm range. The high frequency detection (HFD) techniques might be two times in amplitude compared to the readings during stage one(more wear in the form). Figure3.2 move obvious wear in the form of pits 3.2.3 Stage 3 of bearing defect As we enter the third stage (Figure 3.3), the separated bearing harmonics and frequencies can be seen in the FFT. These may show up with a number of sidebands. Wear is generally now visible on the bearings and may increase through to the edge of the bearing raceway. The small holes of the earlier stage now begin to develop into bigger holes and their numbers increase as well. When well created sidebands come with any bearing defect harmonics. It is frequency and the HFD components have again roughly doubled compared to the stage three. It is frequently advised to replace the bearing in this stage(wear is now clearly on the surface of bearing). Figure3.3 wear is now very clearly Stage 4 of bearing defect In the final phase (Figure 3.4), the lowest points merge with each other, generating rough tracks and spelling of the bearing raceways and rolling elements. By now, bearings are in deeply damaged condition and even the amplitude will rise. As it grows, it might also increase many running speed harmonics. It can be envisaged as higher clearances in the bearings allowing a higher displacement of the rotor. Separated bearing fault frequencies and bearing component normal frequencies in reality begin to merge into a random, broadband high frequency ‘noise level’. Firstly, the average amplitude of the broad noise may be very large and it will drop; the width of the noise will be high. The amplitude will rise again in the last stage. However, amplitudes of high frequency noise level and some of the HFD might in fact reduce (due to lowest points flattening to become spalls), but just enough to raise previously failed spike energy to great amplitudes. Figure 3.4 :(damaged bearing in final stage of wear) By this time, the bearing will be vibrating vigourously; it will be extremely hot and making lots of noise. If it is allowed to run further, the rolling elements will break lose and the cage will break. The elements may then run into each other, until the machine hopefully trips on overload. In all probability, there will be serious damage to the shaft area which is under the bearing. 3.2.1 Time Domain Vibration data plotted as amplitude against time is indicated as time domain data. Some plots have been shown in Figures x. An example of complication of these data for an effective piece of industrial machinery has been shown in Figure 3.4. Time-domain plots need to be used for all reciprocating and linear motion machinery. They are helpful in thorough analysis of machine trains to study what changes happen in running condition. The data for Time domain are not easy to use. Because the vibration data in this kind of plot is added to represent the total displacement at any Time could be given; it is hard to find the contribution of specific vibration source. Some types of data, which are regularly taken through the life of machine are directly similar to historical data taken at the same load as well as speed. This is not practical because of differences in time to time changes in speed and plant operations .This noticeably has an impacts on the profile. (Time domain signature) Figure 3.4 3.2 Frequency Domain From an effective viewpoint, the basic harmonic vibration functions are connected to frequencies of the moving or rotating components, however, frequency domain amplitude could be the displacement per unit time contacting to an exact frequency, which is shown in figure as the Y-axis against frequency as the X-axis. These frequencies are several of the general running speed of the machine train in rpm. The data of frequency domain are acquired by changing time-domain data using a mathematical technique, like (FFT).FFT lets each vibration element of a complicated machine train spectrum to be shown as a separate frequency peak. This is completely different to the time domain spectrum. Figure 3.5 . 3.3 Interpretation of Vibration Data The ability to distinguish between abnormal and normal vibration profiles is the solution to using vibration signature analysis for diagnostic, prognostic maintenance and other applications. Some vibrations are normal for a piece of moving or rotating machinery. Particular problem with machinery generate abnormal vibrations. Examples for that are lose bolt, worn bearings, misalignment, leaks etc. 3.3.1 types of vibration transducers An analysis is only as perfect as the data used, equipment used to get the data are critical in finding the fault or success of a predictive credible amelioration program. Three essential types of vibration transducers could be used for monitoring the mechanical condition: displacement probes, accelerometers and velocity transducers. Each one of these has restrictions and particular applications for which its use is suitable. 3.3.1.1 Displacement Probes Eddy-current or Displacement probe is designed to measure the effective displacement or movement of a machine’s shaft which relates to the probe. Lastly, increased displacement probes supply the most accurate data on machines having a rotor weight that is very low in relation to support structure and the casing. Large compressors in different types of plant equipment should have displacement transducers mounted at key measurement locations. The useful frequency range for displacement probes is from 10 to 1000 Hz, or 600 to 60,000 rpm as figure is shown blow. (displacement) Figure 3.6 3.3.1.2 Velocity Transducers Velocity Trnsducers are electromechanical sensors designed to render vibration or monitor casing, different from displacement probes. The main limitation of velocity transducers is their thermal damage and sensitivity to mechanical, velocity transducers measure the rate of the range (displacement) rather than the distance of movement. The regular use can cause a loss of calibration; a strict recalibration program is required to stop or avoid data errors. (1.pickup case 2.wire out 3.damper 4.mass 5.spring) Figure 3.7: Velocity Transducers 3.3.1.3 Accelerometers Acceleration is probably one of the best methods of determining the force resulting from machine vibration. Accelerometers use piezoelectric films or crystals to change mechanical energy into electrical signals (Figure 3.7). The advantage of use of accelerometer is that it does not require a calibration program to guarantee the existing accuracy, however, they are vulnerable to thermal damage. If enough heat radiates into the piezoelectric film, it could be destroyed or damaged. Thermal damage is scarce (in short supply) because data acquisition time is relatively short by using provisional mounting technique. Figure is shown below (1.pase 2. Piezoeletric crystals 3.mass 4.case) Figure 3.7 1.1 Classification of rolling bearing Rolling bearings consist of two central classifications: roller bearings and ball bearings. They are classified on the basis of their bearing ring configurations: roller bearings, deep, groove, thrust, angular contact types. Figure 1.1 On the contrary, bearings could be classified depending on the shape of rollers: needle, cylindrical, spherical, and tapered. Rolling bearings could be classified depending on the direction according to the load that is applied; thrust bearings carry axial loads and radial bearings carry radial loads. There are other classification methods in addition to the above mentioned, number of rolling rows (non-separable, in which either the outer ring or the inner ring could be separated), single, double, or four row, the thrust bearings that could carry axial loads in just one direction, and the double direction thrust bearings could carry loads in two directions. There are also many types of bearings designed for special functions, for example ball screw support bearings, journal roller bearings and linear flat roller bearings. 5.1 INTRODUCTION Rolling element bearing is an essential part of nearly all machinery transmitting power. It’s used in ships, airplanes, space vehicle, small and large industrial factories, etc. The extent of loss or damage could be observed when the bearing abruptly falls off an airplane after achieving its maximum height, off a ship in the sea, off a space vehicle, or when vehicles overtake other vehicles, or fails in industrial factories during it’s highest degree of production run. The test of rolling element bearings include a range of parameters to be considered as the level of load and speed, kind of loading, like axial or radial or both and different types of lubricants. The optimal test rig basically consists of main mover with speed control selections, a supporting shaft assembly to get together for test bearing with selections to integrate multiple diameter bearings, the arrangement for application of load, such as mechanical or hydraulic, measuring devices connected to parameters to be measured 5.4 data acquisition Data acquisition (DAQ) is the development of measuring physical or electrical components, such as current, temperature, voltage, sound or pressure. PC based data acquisition uses a series of units, flexible software and hardware to transfer it to the desktop computer or standard laptop into a user control system defined measurement. Whereas every data acquisition system has exclusive functionality to initiate particular requirements, all systems share components that include, sensors, signal conditioning, and a computer with software. The data acquisition software has been used to convert the Isomorphic data into a digital series; this software is a Schematic programming language. The figure 5.4 shown the data acquisition which has been used(modelYE6232) 5.5EXPERIMENTAL SETUP As in Figure 5.5, the test consists of two parts: electrical and mechanical. Electrical part of the test has 3 three parts: three current transformers, the PC-based data acquisition card, an outlet and small monitoring that are used to control the load, speed and time duration. Mechanical part consists of induction motor and generator for speed measuring and a break to load the induction motor and reduce the speed. Figure 5.5 Figure5.6 The experimental setup have been shown through the above figure and describes the graphics of the setup. In the photograph of the experimental test rig setup, damaged ball bearing was pleased in position 3 and the bearing fault signals were from position 2 and 3 with different speeds. That has been used in this study. The test rig basically consists of the shaft supported at one end by two placed bearings (press fixed in a steel housing at either ends) and suspended at the other side of the end that supports the test bearing. The end of the shaft is coupled to motor (DC) and the other end carries the test bearing. The steel shaft is fitted above insulating sleeve on which the steel sleeve of rotable length is strongly fitted. The major diameter of steel sleeve is the same to the inner diameter of bearing. Similarly, inner diameter of steel sleeve is the same as outer diameter of the insulating sleeve. The outer diameter of the steel sleeve different from one to the other end; just below 30mm on one and just above 30mm at the other end, after considering different tolerances. The steel sleeve was sharpened or tapered to make the removal and fitting of the several test bearing with inner diameter of 30 mm easy. The test bearing are cylinder roller bearings (n406) 5.6 Accelerometer The essential construction of the accelerometer has been put on view schematically in figure with schematic drawing of accelerometer. The transuding element consists of piezoelectric dices which are held between a base and mass. The mass will apply a changeable force on piezoelectric disc when the accelerometer is subjected to vibration and a changeable probable will be developed across the element. The potential will be directly comparative to the acceleration over a specific frequency range. The most important Characteristics of piezoelectric material of an accelerometer are piezoelectric constant, dielectric, resistance stiffness, and curie point. The class of sensitivity of accelerometer can be defined as the ratio of accelerometer’s electrical output to the mechanical input. In this experiment, the accelerometer is used to measure the vibration of the test bearing. The transducer has been designed for study mounting and its case ground is secluded from the mounting surface. Two different accelerometers have been used in this test, namely Channel 1 (ca-yd -185tnc) 00016 and other channel using (CA-YD-185TNC) 00039 accelerometer code; the two configurations are shown as the charge per unit accelerometer in figure 6.5 shown below. Figure 5.6 :Accelerometer 5.8 Matlab Software The general function of matlab is language programming. When matlab is used to process images one normally writes function files, or writing files to carry out the operations. These files form a formal proof of the process being used and to make sure that the last results could be replicated and tested by others, should the need come up. Matlab can offer so many functions for image processing and other odd jobs. Most of these jobs are written in the Matlab language and are overtly readable, such as basic text files. Therefore, the achievement details of these functions are easily reached and open to study. The protectionist could inspect the processing used in full detail, and any challenge raised could be taken action against in an informed way by the prosecution. This makes the Matlab dissimilar from applications, like Photoshop. The Matlab might not be as user friendly as an application, like Photoshop, being a universal tool for the purpose of language programming; it offers some important advantages for image processing. It becomes a guarantee that the image processing stages used are totally documented, and thus could be replicated. In general, the basis code for all image processing functions are easily reached for inspection and test. The Matlab assures that geometric precision is maintained all the way through the increase or enhancement process. Image processing algorithm available by using Matlab are expected to be more advanced than other image processing applications, below figure shows time domain for two channels by using Matlab software. 6.2.1Healthy bearing(normal test) Normal test which means that the load has not been applied or the shaft is rotating at a maximum speed (1470 rpm) and the shaft frequency would not change [24.5 Hz] as calculated in previous chapter. This shaft has been supported by two bearings – one, a healthy bearing and the other being faulty bearing(outer race defect). The spectrum has filtered data in the frequency domain as well as the raw data in the time domain. This has been shown in figure 5.1; the outer race fault signal is shown in red color and the healthy signal of the bearing is shown in blue color. Figure 6.1:Spectrum of raw data for outer race fauty bearing and healthy bearing. The waveform in Fig 6.1 gives an impression that it is the overall vibration level of the signal, outer race fault is (large) with a peak value of 0.7191 and RMS value of 4.568, and healthy signal bearing has got low level of vibration with a peak value of 0.1911 and RMS value of 3.953. The waveform is not clear with small value of amplitude as shown in blue color and high amplitude shown in red color that belongs to outer races signal (faulty signal). The result that showed in case of both bearings with healthy bearing and outer race defect, the signal from the healthy bearing shows very clear waveform of the normal amplitude, which was achieved by applying a band pass filter to the high frequency level. FFT was also applied to the envelope spectrum to observe if there is any abnormal frequency. However, It has been shown as few harmonics of shaft speed frequency in Figure 6.2. Figure 6.2 The frequency for outer race fault has been calculated in previous chapter, which is 84.1 Hz. Figure 6.3 shows the signals of the outer race fault with a high level of amplitude in the time domain acquired by applying a band pass filter to the high frequency range, the components were visible in the spectrum when FFT was applied to the envelope spectrum, the outer race frequency is estimated to be approximately 90.82hz. Figure 6.3 Outer race is shown in plots and is clearly evident in figure above. 6.2.2 Roller element fault, shaft looseness fault &healthy bearing For this type of test, the bearing is made to run at (1470) rpm and 50% of the load was applied as in the Figure 6.4, the roller fault signal and looseness fault is shown in red colure, and the healthy bearing signal is shown in blue colour. Figure 6.4 The waveform does not look very clear (two signals at the same time). The peak value for the roller fault is 0.237 with a RMS of 4.38. In the other view, healthy bearing has an RMS value of 3.024 with a peak of 0.0871, by Applying FFT for the raw data. The spectrum of filtered data the raw data are very clear in the bottom of the figure with clear signal. Figure 6.Error! No text of specified style in document. The roller fault is 97.4hz as calculated in previous chapter and the lossless fault frequency for the second harmonic is (1.5 x) when( x ) is shaft frequency, which is x=23.44 Hz, and 1.5X=35.16 HZ. This is exactly the same with the frequency in second harmonic for lessens fault as shown below in Figure 6.6. The vibration signal for lessens fault with a high amplitude in the time domain is measured by applying a band pass filter to high frequency range. The components were visible in the spectrum as well, when using FFT to envelope spectrum, the roller fault frequency is estimated to be approximately 99.61Hz with amplitude of 0.1541 and the looseness fault frequency is approximately 35.16hz with amplitude 0.08758hz in the second harmonic as shown in figure 6.6. Figure 6.7 show comparison of vibration spectra around roller fault. . Figure 6.6 Figure 6.7 6.2.3 The inner race fault and healthy bearing The inner race frequency is 136.4hz which was calculated in pervious chapter, the inner race fault is shown in red color in Figure 6.8 and the healthy bearing shown in blue color. (This bearing has two faults inner race and looseness in the shaft body) The defect could be easily detected using the vibration; the peak value for inner race fault is 0.3979 and the healthy bearing has not changed 0.1407. The bottom of the Figure5.4 is shown using the the FFT filtered data in the frequency domain with clear high amplitude. Figure 6.8 The vibration signal for (Time domain) of the inner race fault clear with a high level of amplitude as shown in Figure 6.8 when applying a band pass filter to the high frequency range, however by applying FFT to the envelope spectrum. The inner race frequency is estimated to be approximately 123Hz and the amplitude as 0.2387 and the looseness frequency is close to 38.09hz with amplitude 0.1728hz, both of them are shown in figure 6.9 and figure 6.10.below. Figure 6.9loosens fault frequency (second harmonic) Figure6.10 Chapter 7 7.1Overall Conclusion This study explains the Condition Monitoring of the bearing elements. Actual possible failures are classified for five models of roller bearing signal raw as (inner race, outer race cage, ball and looseness faults) the vibration has been measured by using the software YE7600. The analysis was carried out by using bearing data acquired from the software YE7600; it carried out five kind of failure experiments under different load conditions and the data has been measured by using accelerometer which was positioned on the bearing housing in a horizontal plane. In conclusion, the condition monitoring techniques and signal providing is explained when applied to vibration signals acquired from several faults on the rolling bearings. Read More