An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

Hu, Bingbing; Guo, Chang; Wu, Jimei; Tang, Jiahui; Jia-ling, Zhang; Wang, Yuan

doi:10.1115/1.4043063

Cited by 15 publications

(6 citation statements)

References 41 publications

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“…It can effectively avoid output saturation by independently adjusting system parameters. The first-order MSR system model is expressed as follows [39][40][41] ( ) ( ) ( )…”

Section: First-order Msr System Modelmentioning

confidence: 99%

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

et al. 2021

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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.

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“…It can effectively avoid output saturation by independently adjusting system parameters. The first-order MSR system model is expressed as follows [39][40][41] ( ) ( ) ( )…”

Section: First-order Msr System Modelmentioning

confidence: 99%

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

et al. 2021

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“…With the rise of the swarm intelligence optimization algorithm, finding the global optimal solution through the swarm intelligence algorithm can solve the limitations of traditional adaptive SR systems, and this concept has been extensively used in the domain of bearing fault detection [ 18 ]. However, in the existing research results, the adaptive selection of SR model parameters still depends on the performance of intelligent optimization algorithms, so there are generally issues such as a low solving accuracy and being prone to falling into local optima [ 19 ]. Therefore, the feasible method to effectively enhance the parameter performance of adaptive selection of SR systems is to improve the defects of the intelligent optimization algorithm, so that it can more quickly and accurately optimize the parameters of the SR system.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

Huang

Zhang

2023

Sensors

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In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy–Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.

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“…If the system running status becomes unstable, relevant information measurements, such as vibration and sound, should change accordingly. However, the incipientstage unstable signal is often considered very weak and its duration is short as well [15]. It is easily submerged by the strong environmental noise, making Fourier-based spectral analysis unpredictable in feature representation [14].…”

Section: Introductionmentioning

confidence: 99%

Transient running stability assessment of industrial rotating machinery based on bag of correlated vibration feature representation

Gui¹,

Chen²,

Zhou³

et al. 2022

Meas. Sci. Technol.

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The objective of this paper is to monitor and assess the running stability of industrial machinery operating under transient conditions. A new method called bag of correlated feature representation (BOCFR) is presented in this paper, aiming to extract structural information hidden in the data. At ﬁrst, Bag-of-Words (BoW) model is employed to cluster the collected training samples. For a new observed signal, correlogram is subsequently constructed with BoW to reﬂect its dynamic characteristic. Entropy is then calculated aiming to periodically analyze the dynamic characteristics of machine running status over time. Together with the BOCFR, a uniﬁed framework by means of hypothesis testing, is ﬁnally proposed, which is evaluated based on simulation scenarios and real-engineering applications: comparison results with typical techniques demonstrates the proposed method achieving the promising performance.

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An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

Cited by 15 publications

References 41 publications

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

Transient running stability assessment of industrial rotating machinery based on bag of correlated vibration feature representation

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