International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Wavelet analysis to detect fault in Clutch release bearing Gaurav Joshi 1, Akhilesh Lodwal 2 ME Scholar, Institute of Engineering & Technology, DAVV, Indore, M. P., India Assistant Professor, Dept. Of Mechanical Engineering, Institute of Engineering & Technology, DAVV, Indore ---------------------------------------------------------------------***--------------------------------------------------------------------2. LITERATURE SURVEY Abstract - Automobile clutch release bearings are 1
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important automotive driveline components. An unexpected failure of the bearing may cause catastrophic failure leads to vehicle off road, increased down time and economic losses. For that reason, fault diagnosis in bearing has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of bearings. The vibration signal of a bearing carries the signature of the fault in the outer race, inner race, cage and rolling element. Early fault detection of the bearing is possible by analyzing the vibration signal by using wavelet analysis. Wavelet analysis is able to characterize the local features of the signal in different scales. This project addresses fault diagnosis of ball bearing related to clutch release system. Detail analysis using wavelet methodology is done to find out the faults in MATLAB software. Numbers of faults are identified and validated for each fault. Faults are identified on clutch release bearing vibration test rig.
In 2015 Guoliang Chen & Xiaoyang Chen Studies on Automobile Clutch Release Bearing Characteristics with Acoustic Emission Acoustic emission techniques in contact fatigue failure detection have unique advantages, which include highly sensitive non-destructive testing methods [1]. In 2014 Robert Bicker & Alaa Abdulhady Jaber presented a paper on Simulation of Non-stationary Signal Analysis Using Wavelet Transform Based on Lab VIEW and Matlab. Defines Basic concepts of signal, types of analysis technique and Wavelet analysis which represents an efficient method of time frequency analysis. In this paper, a real-time wavelet analysis system has been designed. Hybrid programming combining Lab VIEW graphical programming with Matlab textural programming has been shown to be an effective method to build an intelligent signal monitoring and feature extraction system [2]. In 2012 D.P. Jena & S.N. Panigrahi presented a paper on Bearing and gear fault diagnosis using adaptive wavelet transform of vibration signals. There work reveals that statistical parameters, even though suitable for defect identification, are sensitive towards detecting their severity. The proposed method of adaptive wavelet design and its implementation provide adequate time frequency information in order to analyze the non-stationary signals [3]. Springer science 2011, A handbook on “Wavelets – Theory and Applications for Manufacturing” by Robert X. Gao , Ruqiang Yan describes the selection criteria of mother wavelet based on Qualitative & Quantitative measure. By selecting a base wavelet that is best suited for analyzing nonstationary signals. These measures are examined from two difference aspects: their corresponding wavelet coefficient i.e. the energy / Shannon entropy and the relationship between the signal analyzed and the coefficients of the base wavelet. Based on this two comprehensive base wavelet selection criteria the maximum energy to Shannon entropy and the maximum information measure are identified as the quantitative measure for determining the best suited wavelet [4]. Matlab 2013, A handbook on “Wavelet Toolbox” by Michel Misiti, Yves Misiti, Georges Oppenheim and JeanMichel Poggi describes an introduction to wavelet, Types of wavelet, Continuous wavelet transformation, scaling, shifting etc in detail [5]. In 2015 Josko Soda & Igor Vujovic presented a paper on Analysis of the vibration signal using Time frequency methods. This paper presents advantages of the wavelet transform for the acceleration vibration signal analysis due to its non-stationary nature [6].
Key Words: Vibration signal, Wavelet analysis, Fault detection, bearing.
1. INTRODUCTION A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is a timefrequency method and applied to non-stationary signals. It is breaking up of a signal into shifted and scaled versions of the original (or mother) wavelet. Wavelets are a recently developed signal processing tool enabling the analysis on several timescales of the local properties of complex signals that can present non-stationary zones. Wavelets have essentially been imposed as a fruitful mathematical theory and a tool for signal and image processing. By omitting the purely mathematical contributions and focusing on applications, we may identify three general problems for which wavelets have proven very powerful. The objective of the project is to make a bearing fault identification system using Wavelet analysis. To investigate the type of bearing failures of a complex clutch release bearing system on a well-designed test rig suited for practical implementations. The advantages of wavelet analysis can be materialized by local detailing, meaning that it can be taken only a part of the signal. By wavelet analysis we can find mechanical failures like fatigue (surface or subsurface, cage wear, handling damage, corrosion pitting, crack etc.
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