Abstract
In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.
Highlights
Due to the harsh working environment, the rolling bearings, which are a core part of rotating machinery, often suffer different damages, which have serious consequences for safety and the economy [1,2,3]
In order to better analyze the relationship between the CMSR output signal and system parameters, the system parameters are fixed, a = 0.5, r = −0.8, the interval of b is set to [0, 10], the step size is 0.1, and the change curve of output signal-to-noise ratio (SNR) with b is obtained as shown in Figure 5a where the output SNR is maximum at approximately b = 6.5
The pre-processed signal is input into the CMSR in order to detect the faults of motor bearings by Fourier transform
Summary
Due to the harsh working environment, the rolling bearings, which are a core part of rotating machinery, often suffer different damages, which have serious consequences for safety and the economy [1,2,3]. These methods can better detect motor bearing faults, but they find it very difficult to detect the faults from weak motor bearing signals under the strong noise For solving this problem, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely the CMSR, is proposed to detect motor bearing faults. In this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely the CMSR, is proposed to detect motor bearing faults In this proposed CMSR, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients, are analyzed in-depth by numerical simulation technology. A novel advancing signal processing method based on coupled multi-stable stochastic resonance is proposed to detect the faults of motor bearings;.
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