Bearing Fault Diagnosis Based on Stochastic Resonance and Improved Whale Optimization Algorithm

Huang, Wenzhun; Zhang, Ganggang; Jiao, Shangbin; Wang, Jing

doi:10.3390/electronics11142185

Cited by 8 publications

(6 citation statements)

References 33 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The characteristic frequency of the bearing outer ring fault [20] is calculated as follows: , although there is a small amount of noise interference around the fault eigenfrequency, the peak value of the spectrum at 105.5Hz is clearer, and the signal-tonoise ratio is -11.71dB, which is an improvement of the signal-to-noise ratio. After the fault signal is processed by the STSR system, the output time-domain waveform and amplitude spectrum are shown in Fig.…”

Section: Experimental Verification Of Deep Groove Ball Bearingmentioning

confidence: 99%

Composite Exponential Potential Multistable Stochastic Resonance System in Weak Signal Detection

Wang,

Zhang,

Yuan

2023

Preprint

View full text Add to dashboard Cite

Based on the classical bistable system model and the Gaussian Potential model, a new composite exponential potential multistable stochastic resonance system (CEMSR system) is proposed to address the issue that it proves difficult to extract weak signals from conventional stochastic resonance systems under a strong noise background. First, enhancing the Gaussian Potential model creates a multistable stochastic resonance system model with a complex power index function. To confirm the impact of noise intensity on the system, the system is numerically solved using the fourth-order Runge-Kutta algorithm. Second, the system parameters are adjusted using the quadratic sampling technique, and the output signal-to-noise ratio (SNR) is employed as the fitness function of the particle swarm optimization process. The suggested model is compared to the system output of two compound tri-stable models by signal simulation and experimental verification, and it is demonstrated that the proposed model performs better at weak signal detection under the condition of fewer system parameters.

show abstract

Section: Experimental Verification Of Deep Groove Ball Bearingmentioning

confidence: 99%

Composite Exponential Potential Multistable Stochastic Resonance System in Weak Signal Detection

Wang,

Zhang,

Yuan

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…When the optimal solution is selected, the search agent position is updated according to p. The WOA can switch between helical and circular motion. Finally, the WOA algorithm is terminated by satisfying the termination criterion [49,51].…”

Section: Search For Preymentioning

confidence: 99%

A Novel Adaptive PID Controller Design for a PEM Fuel Cell Using Stochastic Gradient Descent with Momentum Enhanced by Whale Optimizer

2022

View full text Add to dashboard Cite

This paper presents an adaptive PID using stochastic gradient descent with momentum (SGDM) for a proton exchange membrane fuel cell (PEMFC) power system. PEMFC is a nonlinear system that encounters external disturbances such as inlet gas pressures and temperature variations, for which an adaptive control law should be designed. The SGDM algorithm is employed to minimize the cost function and adapt the PID parameters according to the perturbation changes. The whale optimization algorithm (WOA) was chosen to enhance the adaptive rates in the offline mode. The proposed controller is compared with PID stochastic gradient descent (PIDSGD) and PID Ziegler Nichols tuning (PID-ZN). The control strategies’ robustnesses are tested under a variety of temperatures and loads. Unlike the PIDSGD and PID-ZN controllers, the PIDSGDM controller can attain the required control performance, such as fast convergence and high robustness. Simulation results using Matlab/Simulink have been studied and illustrate the effectiveness of the proposed controller.

show abstract

“…But, the adaptive SR method, which takes a single parameter of the system as the optimization object, often ignores the interaction between the parameters of the system. 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 ].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Bearing Fault Diagnosis Based on Stochastic Resonance and Improved Whale Optimization Algorithm

Cited by 8 publications

References 33 publications

Composite Exponential Potential Multistable Stochastic Resonance System in Weak Signal Detection

Composite Exponential Potential Multistable Stochastic Resonance System in Weak Signal Detection

A Novel Adaptive PID Controller Design for a PEM Fuel Cell Using Stochastic Gradient Descent with Momentum Enhanced by Whale Optimizer

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

Contact Info

Product

Resources

About