A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators

Xueyan Wang; Xueyan Wang

doi:10.3934/mbe.2023392

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 8933-8953. doi: 10.3934/mbe.2023392

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Research article

A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators

Xueyan Wang ^,

College of Intelligent Manufacturing, Zibo Vocational Institute, Shandong, 255300, China

Academic Editor:Yang Kuang

Received: 29 December 2022 Revised: 10 February 2023 Accepted: 15 February 2023 Published: 09 March 2023

In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters. And on this basis, the fuzzy C means algorithm is used to obtain the clustering centers of the fault data, and the network model is reconstructed by selecting the samples closest to the clustering centers as the neurons in the probabilistic neural network. The mathematical analysis and derivation of the T-S (Tkagi-Sugneo) fuzzy neural network-based diagnosis strategy are carried out; the T-S fuzzy neural network-based generator fault diagnosis system is designed. The model is implemented on the MATLAB/Simulink platform for simulation and verification, the experiments show that the T-S fuzzy diagnosis strategy is significantly improved, and the design purpose is achieved. The fuzzy neural network has a parallel structure and can perform parallel data processing. This parallel mechanism can solve the problem of large-scale real-time computation in control systems, and the redundancy in parallel computation can make the control system highly fault-tolerant and robust. The fault diagnosis model based on an improved probabilistic neural network is applied to the fault data to verify the effectiveness and accuracy of the model.
- fuzzy neural network,
- automatic fault diagnosis,
- intelligent analysis,
- permanent magnet synchronous generators
Citation: Xueyan Wang. A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 8933-8953. doi: 10.3934/mbe.2023392

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Abstract

In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters. And on this basis, the fuzzy C means algorithm is used to obtain the clustering centers of the fault data, and the network model is reconstructed by selecting the samples closest to the clustering centers as the neurons in the probabilistic neural network. The mathematical analysis and derivation of the T-S (Tkagi-Sugneo) fuzzy neural network-based diagnosis strategy are carried out; the T-S fuzzy neural network-based generator fault diagnosis system is designed. The model is implemented on the MATLAB/Simulink platform for simulation and verification, the experiments show that the T-S fuzzy diagnosis strategy is significantly improved, and the design purpose is achieved. The fuzzy neural network has a parallel structure and can perform parallel data processing. This parallel mechanism can solve the problem of large-scale real-time computation in control systems, and the redundancy in parallel computation can make the control system highly fault-tolerant and robust. The fault diagnosis model based on an improved probabilistic neural network is applied to the fault data to verify the effectiveness and accuracy of the model.

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