The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.

The objective of this work is to develop techniques to automate the condition-based maintenance procedure. It is observed that vibration signals are capable of alarming the malfunctions in machineries. In order to overcome the shortcomings in the traditional vibration analysis using time-domain and frequency-domain features, two new approaches based on wavelet transform, artificial neural network and fuzzy rules are proposed for detecting and localizing defects in rolling element bearings. The two expert systems are developed and tested with the use of vibration signals collected from the bearing housing of an experimental setup. Experiment results show that the proposed approaches are sensitive and reliable in detecting defects on the outer race, inner race and rolling elements of bearings. The proposed approaches may be used for other fault diagnoses such as gear faults, coupling faults, belts in industries. It is also expected from the obtained results that the generalized defect detection will be easier in future by using the proposed approaches via other parameters such as noise, temperature, lubricant analysis in addition to used vibration signals.

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PurposeThe objective of this 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. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.Design/methodology/approachA detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.FindingsThe development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.Practical implicationsThe work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.Originality/valueThe main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

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