Abstract
Aiming at the problem of low diagnosis efficiency and accuracy, due to noise and cross aliasing among various faults when diagnosing composite faults of rolling bearing under actual working conditions, a composite fault diagnosis method of rolling bearing based on optimized wavelet packet autoregressive (AR) spectral energy entropy and adaptive no velocity term particle swarm optimization-self organizing map-back propagation neural network (ANVTPSO-SOM-BPNN) is proposed. The energy entropy feature is extracted from the bearing vibration signal through wavelet packet AR spectrum, and SOM and BPNN are combined to form a series network. For PSO, the velocity term is discarded and the inertia weight and learning factor are adaptively adjusted. Finally, the Dempster-Shafer (D-S) evidence fusion diagnosis is carried out. To get closer to the application condition, the data are collected near and far away from the fault point for the composite fault diagnosis, which verifies the effectiveness of the proposed method.
Highlights
Rolling bearing is one of the most important components in rotating machinery
This paper proposes a new diagnosis method based on optimized wavelet packet AR spectral energy entropy to adaptive no velocity term particle swarm optimization (PSO)-self-organizing map (SOM)-back propagation neural network (BPNN) (ANVTPSOSOM-BPNN)
Because the parameter setting of PSO has a great impact on the final result, this paper adopts an adaptive way to adjust the inertia weight and learning factor of PSO and round off its velocity term to avoid the influence of particle initial velocity on the convergence speed and solution accuracy, which is the new ANVTPSO algorithm, used for SOM-BPNN threshold and weight optimization, to improve the accuracy of fault diagnosis
Summary
Rolling bearing is one of the most important components in rotating machinery. It plays an important role in supporting rotating shaft and reducing friction. Tang and Deng [8] proposed a composite bearing fault feature separation method based on the improved harmonic wavelet packet decomposition to decompose the signal of intermediate frequency part and extract more effective signals. He et al [9] applied the adaptive redundant multiwavelet packet to composite fault diagnosis of rotating machinery, proposed the normalized multifractal entropy as the evaluation criterion, adaptively constructed multiwavelet, and determined the fault sensitive frequency band by the relative energy ratio of characteristic frequency. The proposed method is used to fuse the diagnosis results at two measuring points at D-S evidence decision level to improve the efficiency and accuracy in the composite fault diagnosis of rolling bearing
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