International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),International Journal of M...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),   ISSN 0976 – 6359(Online...
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Estimation of misalignment in bearing shaft by signal processing of acoustic signal

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Estimation of misalignment in bearing shaft by signal processing of acoustic signal

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),International Journal of Mechanical EngineeringISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEand Technology (IJMET), ISSN 0976 – 6340(Print)ISSN 0976 – 6359(Online) Volume 2 IJMETNumber 1, Jan - April (2011), pp. 60-69 ©IAEME© IAEME, http://www.iaeme.com/ijmet.html ESTIMATION OF MISALIGNMENT IN BEARING SHAFT BY SIGNAL PROCESSING OF ACOUSTIC SIGNAL Naveen Rathi1, Amit Gupta2, B. Manpreet3, Rajesh Kumar4 , Vikas Kumar5 1 Pursuing M.Tech from NCCE ,Israna, Panipat, Haryana, India er.naveenrathi@gmail.com 2 Department of Mechanical Engineering, NCCE ,Israna, Panipat, Haryana, India eramit81@yahoo.co.in 3 Pursuing PHD from SLIET Longowal -148106 (Pb), India bainscoolbains@yahoo.com 4 Department of Mechanical Engineering, SLIET Longowal -148106 (Pb.), India 5 Pursuing M.Tech from NCCE ,Israna, Panipat , Haryana, India vikas4m@gmail.comABSTRACTAcoustic Emission (AE) is being extensively used as a Non Destructive Technique (NDT) fordiagnosis of rotating components. The main theory behind this diagnosis is that while rotation,acoustic energy level of defective portion in rotating element is different. In the present study asignal processing technique is proposed to identify the variation in acoustic signal and results areverified for misalignment of the bearing shaft. Two different functions Fast FourierTransformation (FFT) and Decomposition are used to represent the Acoustic signal at differentlevels of misalignment. Result reveals that the proposed methods are effective in estimating themisalignment present in the bearing shaft. Statistical parameters such as Standard deviation andShannon entropy are also increases with increase in misalignment in the bearing shaft.Keywords: acoustic emission, condition monitoring, fault diagnosis bearing, misalignment ofbearing shaft, signal processing.I. INTRODUCTIONVibration analysis is widely used in diagnostics of faults in machinery. There are many analyticaltechniques such as Resonance demodulation [1], Instantaneous power spectrum distribution[2] andConditional moments analysis[3] etc which have been developed for processing vibration signalsto obtain useful diagnostic information about processing gear faults. Over the last few decades thevibration analysis by using acoustic condition monitoring of rotating component has received verylittle attention. This was probably due to perception that monitoring of air borne sound from amachine is noisy and complex in normal industrial environment. During the last few years asignificant process in the capability of acoustic instrumentation together with the signal processingtechniques has made it possible to extract useful diagnostic information from contaminatedacoustic signals. Number of signal processing techniques like by using Discrete wavelettransform,, Morlet wavelets, and Wavelet transform etc have been developed to diagnose thefaults in bearing and removal of noise from the signal in other industrial and research applications[4-10]. 60
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEIn the present work experiments are conducted to identify misalignment of bearing shaft byrecording the acoustic signal in the computer. The recorded acoustic signal is processed inMatlab6.0 by using two different functions ‘Fast Fourier Transformation ‘(FFT)’ and‘Decomposition’ for the analysis purpose.II. THEORYA .Fast Fourier Transformation (FFT)The FFT is essence, decomposes or separates a waveform on function into sinusoids of differentfrequencies It identifies or distinguishes the different frequency sinusoids and their respectiveamplitudes [4]. The Fourier transform f(s) of function f(x) is expressed asf(s) = ∫ f (x) exp (-i 2πxs) do. (1)Applying the same transform to f(s) gives ∫f (w) =∫ f (s) exp (-i 2πws) ds. (2)If f(x) is an even function of x, that is f(x) = f(-x), then f(w) = f(x). If f(x) is an odd function of x,that is f(x) = -f (-x), then f(w) = f(-x). When f(x) is neither even nor odd, it can often be split intoeven and odd parts. It is often useful to think of functions and their transforms are occupying twodomains which are called as upper and lower domains.B. Wavelet decompositionMany applications use the wavelet decomposition taken as a whole. The common goals concernthe signal or image clearance and simplification, which are parts of de-noising or compression.When trying to classify the applications by domain, it is almost impossible to sum up severalthousand papers written within the last 15 years. The decomposition process can be iterated, withsuccessive approximations being decomposed in turn, so that signal is broken down into manylower resolution components. This is called the wavelet decomposition tree. The original signal‘S’ is broken down to lower resolution components like ‘cA1’ and ‘cD1’. Then is further brokenout ‘cA2’ and ‘cD2‘as shown in Figure 2.1. Looking at a signal’s wavelet decomposition tree canyield valuable information as shown below in Figure 2.2Since the analysis process is iterative, in theory it can be continued indefinitely. In reality, thedecomposition can proceed only until the individual details consist of a single sample or pixel. Inpractice, you’ll select a suitable number of levels based on the nature of the signal. Figure 2.1: Wavelet decomposition tree Figure 2.2: Wavelet decomposition tree with valuable information 61
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEIn our work we have used Daubechies 4th order is used as mother wavelet, which is compactlysupported orthogonal wavelet. Daubechies wavelet allows specific parts of the spectrum to befiltered and can potentially give more detail in the time series, compared to the conventional filter[8].III. EXPERIMENTAL SET UPIn this paper we diagnose the defect of misalignment in the bearing shaft. An experimental setup ismade for the analysis purpose. We have taken the cage bearing (NBC make, 6004)having 9 ballsfor our study. A motor (200 watt) is used to drive bearing arrangement and a mike (Logitec make,20 Hz to 16000 Hz frequency range) is used to record the acoustic signal generated by the bearinghousing by placing it 1cm apart from that. In first set of reading shaft is made to run at 1460 rpmwith he help of motor and without any misalignment by providing support at pulley which ismounted at the other end as shown in figure 3.1. Later weight is applied to introduce themisalignment in the shaft as shown in figure 3.2 at three different levels by own weight of shaftand pulley, adding further 2kg and adding 5kg (in total), which is also measured in terms of angleas 0.35, 0.72 & 1.04 degrees respectively. Signal (in wav format) generated by the bearing isrecorded for 2 sec duration with the help of mike and saved in the computer for the processing.These signals then processed in the Matlab6.0 software for generating the raw signal, FFT(FastFourier Transformation) and decomposition at 6th level by using db4 as mother wavelet. Which areshown in the next section. Figure 3.1 Experimental se up with support at the end Figure 3.2: Experimental setup with induced misalignment 62
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEIV. RESULTS AND DISCUSSIONSRaw signal (amplitude vs time), Fast Fourier Transform FFT(energy vs frequency domain) aredrawn and compared in this section. A simple raw signal of case having no misalignment is shown in Figure 4.1 and the other casesof misalignment are having almost similar view of raw signal as in case of no misalignment.FFT’s are also shown below for four different cases given below.Case1: FFT of Shaft without misalignment rotated at 1460 rpm in figure 4.2(a)Case2: FFT of Shaft with misalignment because of its own weight of shaft and pulley or(Equivalent to 1 Kg point load at the pulley) at 1460 rpm in figure 4.2(b)Case3: FFT of Shaft with misalignment with 1.5 Kg additional load at the pulley at 1460 rpm infigure4.2(c)Case4: FFT of Shaft with misalignment with 3.0 Kg additional load at the pulley at 1460 rpm infigure 4.2(d) Figure 4.1: Raw signal of without misalignment at 1460 rpm Figure 4.2 (a): FFT of the signal without misalignment at 1460 rpmFigure 4.2 (b): FFT of the signal having misalignment because of its own weight or Equivalent to 1Kg point load at 1460 rpm 63
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEME Figure 4.2 (c): FFT of the signal having misalignment because of its own weight and additional 1.5 Kg load at 1460 rpm.Figure 4.2 (d) FFT of the signal having misalignment because of its own weight and additional 3 Kg loadThe peaks are coming at the multiple of 24.3Hz which are the harmonics of fundamentalfrequency of rotation, S.No Case Respective Angle of 4th peak height in Misalignment energy value ( mm) 1. Without 135.26 0° 155 misalignment 2. Misalignment by 133.00 0.35° 255 own weight of pulley and shaft 3. Misalignment by 130.58 0.72° 650 1.5Kg additional load 4. Misalignment by 128.50 1.04° 780 3.0Kg additional load Table 4.1This can be calculated by 1460 / 60= 24.3 rotation per second (Hz)We have marked the energy values corresponding to 4th consecutive peak of 24.3 Hz (ie 97.2Hz)on each FFT graph and their respective values are measured as 155, 255, 650, and 780 for the 64
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMECase1, Case2, Case3 and Case4 respectively, Which are described in Table 4.1 and shown inFigure 4.3 (Graph between Energy of 4th consecutive peak of rotational frequency vs Angle ofmisalignment ).The trend shown by energy level at 97.2Hz (4th harmonic of 24.3Hz) frequency in FFT graph isthe clear indication of misalignment, as misalignment increases energy level increases. So the 4thconsecutive peak of rotational frequency must be observed very precisely to avoid the damagescaused by the misalignment. 900 y 800 c n e u q 700 e r f l a n 600 o i t a t o r f 500 o k a e 400 p e v i t u c 300 e s n o c 200 h t 4 f o y 100 g r e n E 0 0 0.2 0.4 0.6 0.8 1 1.2 Angle of misalignment Figure 4.3: Graph between Energy of 4th consecutive peak of rotational frequency vs Angle of misalignmentDecomposition up to 6th level using db4 as mother wavelet is drawn for the Case1 (Withoutmisalignment) and shown in Figure 4.4 Enlarged views for the 6th level decomposition i.e. ’a6’ forthe different cases are shown in the Figures 4.5(b), 4.5(c), 4.5(d) and 4.5(e) for the Case1, Case2,Case3 and Case4 respectively. Impression of each ball can be clearly visible on these enlargedgraphs in terms of peaks as shown in Figure 4.5(a). Also for the case of without misalignment (at1460 rpm) we have marked two point by writing their coordinates which represent the same ballimpression after completing one cycle shown in Figure 4.5(b). With the help of these data pointwe can also calculate the time to complete one cycle and hence rpm of the bearing shaft. As 1 secsignal is decomposed into 44100 data point soTime to complete one cycle=(7568.85-5758.93) /(44100)=1809.92 / 44100=0.041041So rpm of the shaft can be given by= [1 / 0.041041] * 60=1461.94 65
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEFigure 4.4 Decomposition up to 6th level by using db4 as mother wavelet without misalignment at 1460 rpm. Figure 4.5 (a): Enlarged view of 6th level Decomposition ‘a6’ for Case1 without misalignment load at 1460 rpm Figure 4.5 (b): Enlarged view of 6th level Decomposition ‘a6’ for Case1 without misalignment load at 1460 rpm 66
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEME Figure 4.5 (c): Enlarged view of 6th level Decomposition ‘a6’ for Case2 having misalignment because of its own weight or Equivalent to 1 Kg pulley load at 1460 rpm Figure 4.5(d): Enlarged view of 6th level Decomposition ‘a6’ for Case3 having misalignment because of its own weight and additional 1.5 Kg load Figure 4.5 (e): Enlarged view of 6th level Decomposition ‘a6’ for Case4 having misalignment because of its own weight and additional 3 Kg load 67
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEMEEach ball gives two peaks one to the upside and one to the lower side, actually lower side peak isdue to the blank space which is coming after ball and gives impression to the lower side. Themiddle 6 peaks difference has been shown in Figure 4.5(b) with energy variation (upper peak andlower peak) have been measured as 0.070, 0.050, 0.025, 0.020, 0.015 and 0.020 as shown in figure4.5(b). The average of this is coming out to be 0.033 in the case of without misalignment.In the Case-2 having misalignment due to pulley and shaft weight the 6 intermediate peaks energyvariation has been measured as 0.20, 0.12, 0.12, 0.10, 0.10, 0.12 and 0.05 respectively as shown inFigure 4.5(c). The average of this is coming out to be 0.127. Similarly in the next two cases Case-3 and Case-4. the average value of energy variation is coming out to be 0.148 and 0.227respectively. S.No Case Respective Angle of Average value of height in Misalignment energy variation ( mm) 1. Without 135.26 0° 0.033 misalignment 2. Misalignment 133.00 0.35° 0.127 by own weight of pulley and shaft 3. Misalignment 130.58 0.72° 0.148 by 1.5Kg additional load 4. Misalignment 128.50 1.04° 0.227 by 3.0Kg additional load Table 4.2 0.25 Average energy variation of 6 intermediate balls 0.2 0.15 Series1 Series2 0.1 0.05 0 0 0.2 0.4 0.6 0.8 1 1.2 Angle of misalignment Figure 4.6: Graph between Average energy variation of 6 intermediate balls vs Angle of misalignmentWe can observe that as misalignment increases then 6th level decomposition of signal shows moredisturbances (more up and down). This is because of increase in energy at each individual ball ofthe bearing because of the misalignment in bearing shaft. So we can represent the misalignment in 68
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 2, Number 1, Jan - April (2011), © IAEME terms of average energy variation of individual balls shown in Table 4.2 and graph shown in Figure 4.6. So this energy variation of individual balls must be monitored properly to avoid the failures due to misalignment. V. CONCLUSION In this paper we have analyzed the fault of misalignment in bearing shaft by using acoustic signal and developed a method to identify such defects. From the experiments following conclusions can be drawn. 1.It is demonstrated that, although the environment influences acoustic signal for condition monitoring, it does not significantly reduce the extraction of useful diagnostic information. It has been demonstrated that acoustic condition monitoring can effectively be used for detection of misalignment in bearing arrangement. 2.The graphs drawn by using FFT and Decomposition functions responded equally for the fault misalignment. Also the signs of increasing misalignment can be noted down clearly with both functions. 3.In vibration monitoring using acoustic signal have certain advantages over the conventional vibration measuring techniques. Firstly in this sensors do not alter the behavior of the machine due to its non contact nature. And time based information is not lost in this method. 4.Acoustic based method provides considerable freedom in positioning of the microphone. For instance, in this application, small variations in distance and plane of the microphone with respect to the bearing had a little influence in detecting the main characteristics of the bearing acoustics. On the other hand, small change in the location of the accelerometers based method had a bigger impact in detecting the main characteristics of the bearing vibration. 5.The method developed in the project can be used for the condition monitoring and for predictive maintenance of the ball bearing for the misalignment. REFERENCES 1. Wenyi Wang, “Early detection of gear tooth cracking using the resonance demodulation technique”, Mechanical Systems and Signal Processing (2001) 15(5), 887-903 2. Isa Yesilyurt, “Fault detection and location in gears by the smoothed instantaneous power spectrum distribution”, NDT&E International 36 (2003) 535–542. 3. Isa Yesilyurt, “The application of the conditional moments analysis to gearbox fault detection— a comparative study using the spectrogram and scalogram”, NDT&E International (2004). 4. MATLAB User Guide, The Math Works, Inc. (1999). 5. Prabhakar S., Mohanty A. R., and Sekhar A. S., “Application of discrete wavelet transform for detection of ball bearing race faults”, Tribology International 35, 793-800 (2002). 6. Nikolaou N. G., and Anthoniadis I. A., “Demodulation of vibration signals generated by defects in rolling element bearing using complex shifted morlet wavelets”, Mechanical systems and signal processing 16, 677-694 (2002). 7. Jena D. P., Kumar Navneet, and Kumar Rajesh, “Defect detection in bearing using wavelet transform”, NCMC-2003, Patiala (India) 31 October to 01 November 2003. 8. Daubechies I., “Ten lectures on wavelets”, Society for Industrial and Applied Mathematics, Philadelphia (1992). 9. Wu Jian-Da, Huang Chin-Wei, and Huang Rongwen, “An application of a recursive Kalman filtering algorithm in rotating machinery fault diagnosis”, NDT&E International, (2004).10. Kumar R., Singh S. K., and Shakher C., “Wavelet filtering applied to time-average digital speckle pattern interferometry fringes”, Optics and Laser Technology 33, 567-571 (2001). 69

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