Abstract
Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.
Highlights
Mechanical fault diagnosis includes condition detection, fault prediction [1, 2], and analytical diagnosis [3]
To prevent particles from falling into local extremes when searching for targets, the improved particle swarm optimisation (IPSO) algorithm is introduced mutate some particles after each iterative update, i.e., some particles are randomly extracted and reinitialised. e mutation operator expands the search space that constantly shrinks due to the iterations and helps particles trapped in local extremes to break free of their constraints and improves the accuracy of the search targets
Each mode obtained via Empirical mode decomposition (EMD) has aliasing and false modes, as seen in Figure 6. erefore, the decomposition mode performance with empirical wavelet transform (EWT) is preferred over the EMD. e proposed ensemble methodology supplies a feasible plan for nonstationary signal analysis. e effectiveness of the proposed technique is illustrated with three practical examples given
Summary
Mechanical fault diagnosis includes condition detection, fault prediction [1, 2], and analytical diagnosis [3]. The empirical wavelet transform (EWT) was proposed by Gilles [8], which combines the wavelet transform and empirical mode decomposition It is an adaptive time-frequency analysis algorithm and suitable for processing nonstationary signals. Wang [12] proposed an enhanced vibration signal denoising method based on the dual-tree complex wavelet transform and NeighCoeff shrinkage, which could effectively remove noise. Xiang [14] proposed a hybrid approach using a probabilistic principal component analysis and spectral kurtosis to detect rolling element bearing faults. E results indicate that their proposed method effectively detected faults for the rolling element bearing. Hu [16] proposed an improved morphological filter (MF) algorithm to denoise a signal and obtain the fault features from low SNR signals.
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