INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 4, APRIL 2013 ISSN 2277-8616 138 IJSTR©2013 www.ijstr.org Application Of Wavelet Transform For Fault Diagnosisof Rolling Element Bearings P. G. Kulkarni, A. D. Sahasrabudhe Abstract:- The rolling element bearingsare most critical components in a machine. Condition monitoring and fault diagnostics of these bearings are of great concern in industries as most rotating machine failures are often linked to bearing failures. This paper presents a methodology for fault diagnosis of rolling element bearings based on discrete wavelet transform (DWT) and wavelet packet transform (WPT). In order to obtain the useful information from raw data,db02 and db08 wavelets were adopted to decompose the vibration signal acquired from the bearing. Further De- noising technique based on wavelet analysis was applied. This de-noised signal was decomposed up to 7th level by wavelet packet transform (WPT) and 128 wavelet packet node energy coefficients were obtained and analyzed using db04 wavelet.The results show that wavelet packet node energy coefficients are sensitive to the faults in the bearing. The feasibility of the wavelet packet node energy coefficients for fault identification as an index representing the health condition of a bearing is established through this study. Index Terms: - condition monitoring, de-noising, discrete wavelet transform, rolling element bearings, thresholding, vibration, wavelet packet transform. ———————————————————— 1 INTRODUCTION Prompt diagnostics of rolling element bearings fault is critical not only for the safe operation of machines, but also for the reduction of maintenance cost.The vibration based signal analysis is one of the most important methods used for condition monitoring and fault diagnostics of rolling element bearings because the vibration signal always carry the dynamic information of the system. The selection of proper signal processing technique is important for extracting the fault related information. Over the years with the rapid development in the signal processing techniques, for analyzing the stationary signals, techniques such as Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT) are well established. Fourier analysis is one of the classical tools to convert data into a form that is useful for analyzing frequencies. The Fourier coefficients of the transformed function represent the contribution of each sine and cosine function at each frequency. Tandon and Choudhury [1] presented a detailed review of vibration and acoustic measurement methods for detection of defects in rolling element bearings. They have considered both localized and distributed defects. Pitting, spalling etc. are the examples of localized defects while waviness, surface roughness, misaligned races are the examples of distributed defects. Detailed description of these defects is available in standard books on bearings [2, 3]. McFadden and Smith [4, 5] have developed a model to describe the vibrations produced by a single pointdefect and multi point defects on the inner race of a rolling element bearing under constant radial load. It was concluded that frequency components related to the element passing frequency were not the largest components in each group in the spectrum of multi point defects.In addition to local and distributed defects causing vibrationin bearings, variation in stiffness of bearings give rise to vibration. Different causes of bearings vibration are discussed in [6].Sunnersjo [7] has carried out study on the effect of varying compliance on vibrations of rolling bearings with emphasis on radial vibrations with positive clearance. In addition to FFT spectrum analysis, various researchers have used time domain methods for vibration monitoring of rolling bearings.Tandon [8] has compared vibration parameters such as overall RMS, peak, crest factor, cepstrum etc. for the detection of defects in rolling element bearings. Heng and Nor [9] carried out the statistical analysis of sound and vibration signals for monitoring the condition of rolling element bearings. The main drawback of the statistical analysis for rolling bearings is inability to identify the location of faults.Su and Lin [10] extended the vibration model developed by McFadden and Smith to describe the bearing vibration under diverse loading. They have reported the need of time domain analysis alongwith frequency domain to reliably monitor a running bearing.McFadden and Smith [11] explained the step by step procedure of applying high frequency resonance technique (HFRT) for bearing defect detection. This study shows that conventional spectrum analysis cannot detect bearing defects in the presence of vibration from gear and other machine elements. JayaswalPratesh et al.[12] investigated the feasibility of FFT and band-pass analysis for fault detection in rolling element bearings with multiple defects. Fourier transform approach works fine for the analysis of signals that are produced by some periodic process. Most of the signals encountered in practice are finite and aperiodic. The discrete Fourier transform is difficult to adapt to such practical situations. Secondly, this technique has limited success when the signal is buried in background noise or when the signal-to-noise ratio is small. Wavelet Transform (WT) has been viewed as an attempt to overcome shortcomingsof Fourier transform. The basic idea is to choose a basis function having zero mean called “mother wavelet”. Peng and Chu [13] presented a detailed review on the application of wavelettransform in machine fault diagnostics. Wavelet transform while performingtime- frequency analysis is best suited to extract fault features, de- noising and extraction of weak signals and singularity ————————————————  P. G. Kulkarni is currently pursuing Ph. D. degree program in Mechanical engineering in University of Pune, India, PH-+919890265462. E-mail: prof.pgk@mail.com  A. D. Sahasrabudhe is Director, College of Engineering, Pune, India, PH-+919423582025. E-mail: director@coep.ac.in