Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review

Abstract Fault diagnosis through vibration analysis is a method widely used by maintenance engineers for the condition monitoring of rotating machinery. Conventional vibration signature analysis techniques have been used, and after the invention of soft computation, techniques such as artificial neural network (ANN), fuzzy logic, wavelet transform, genetic algorithms, are broadly used by several researchers. These techniques show tremendous scope in the field of fault diagnosis through vibration analysis. The objective of the present paper is to provide a brief review of recent developments in the area of applications of ANN, fuzzy logic and wavelet transform in fault diagnosis. Special attention is given to the rolling element bearings fault diagnosis through vibration analysis, due to the fact that bearings are among the most important and frequently encountered components in rotating machinery.

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