Application of the Laplace-Wavelet Combined With ANN for Rolling Bearing Fault Diagnosis

Abstract

A new technique for an automated detection and diagnosis of rolling bearing faults is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using Laplace-wavelet transform for features’ extraction. The extracted features for wavelet transform coefficients in time and frequency domains are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The Laplace-Wavelet shape and the ANN classifier parameters are optimized using a genetic algorithm. To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for features’ extraction. The results for both real and simulated bearing vibration data show the effectiveness of the proposed technique for bearing condition identification with very high success rates using minimum input features.

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

“…Yang et al [ 16 ] proposed that vibration signals can be decomposed to stationary intrinsic mode functions (IMFs), and the input of ANN is the energy features extracted from IMF so as to identify the rolling bearing failure. Al-Raheem et al [ 17 ] proposed a new technique that used genetic algorithm to optimize the application of Laplace wavelet shape and classifier parameters of ANN for bearing faults. In addition, Shuang et al.…”

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

“…The complexity of combined method May be difficult to interpret Merging two or methods may result in a time-consuming and/or power-consuming issue FDD [127][128][129][130][131][132][133][134][135][136][137][138][139], [141][142][143][144][145][146][147][148][149][150][151][152] Fault prognosis [126], [140] Although the deep learning is not a new concept, it has only recently started to gain more attention and to be successfully applied in different fields, such as computer vision, language and audio processing, and (automatic) recognition [153], [154]. It is only in the last few years that deep learning started to be applied to the PHM field [155][156][157].…”

A Wavelet Spectrum Technique for Machinery Fault Diagnosis

“…In frequency-based analysis, the bearing fault is detected by checking if there exists a pronounced spectral component in the resulting spectra that corresponds to one of the bearing characteristic defect frequencies. If the frequency-based fault detection technique cannot enhance the bearing health condition-related spectral components (i.e., making them pronounced or dominant in the spectral maps), other supplementary methods, based on either time-domain or time-frequency-domain analysis, should be properly employed to improve the diagnostic accuracy [2,16]. In our investigation, it is found that when the bearing is in its normal condition, the shaft speed dominates the resulting spectra due to unavoidable imperfections (e.g., system unbalance).…”

“…In bearing fault diagnosis, the WT is a favorite technique, because it does not contain such cross terms as those in the Wigner-Ville transform, while it can provide a more flexible multi-resolution solution than the short-time FT. According to signal decomposition paradigms, the WT can be classified as the continuous WT, discrete WT, wavelet packet analysis, and those WT with post-processing schemes [16][17][18][19].…”