PSO-MCKD-MFFResnet based fault diagnosis algorithm for hydropower units

Xu Li; Zhuofei Xu; Yimin Wang; Xu Li; Zhuofei Xu; Yimin Wang

doi:10.3934/mbe.2023631

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14117-14135. doi: 10.3934/mbe.2023631

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PSO-MCKD-MFFResnet based fault diagnosis algorithm for hydropower units

State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an 710048, Shaanxi, China

Academic Editor: Yiu-ming Cheung

Received: 23 April 2023 Revised: 28 May 2023 Accepted: 16 June 2023 Published: 25 June 2023

Due to the coupling effect of external environmental noise and vibration noise, the feature rate of the original hydroelectric unit fault signal is not prominent, which will affect the performance of fault diagnosis algorithms. To solve the above problems, this paper proposes a PSO-MCKD-MFFResnet algorithm for fault diagnosis of hydropower units (Particle swarm optimization, PSO; Maximum correlation kurtosis deconvolution, MCKD; Multi-scale feature fusion residual network, MFFResnet). In practical applications, the selection of key parameters in the traditional MCKD method is heavily dependent on prior knowledge. First, this paper proposes a PSO-MCKD enhancement algorithm for fault features, which uses the PSO algorithm to search for the influencing parameters of MCKD to enhance the features from the original fault signal. Second, a fault feature diagnosis algorithm based on MFFResnet is proposed to improve the utilization of local features. The multi-scale residual module is used to extract features at different scales and then put the enhanced signal into MFFResnet for training and classification. The experimental results show that our approach can accurately and effectively classify the fault types of hydropower units, with an accuracy rate of 98.85$ % $. It is superior to other representative algorithms in different indicators and has a good stability.
- hydropower unit,
- fault diagnosis,
- maximum correlation kurtosis deconvolution,
- multi-scale feature fusion residual network
Citation: Xu Li, Zhuofei Xu, Yimin Wang. PSO-MCKD-MFFResnet based fault diagnosis algorithm for hydropower units[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14117-14135. doi: 10.3934/mbe.2023631

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Abstract

Due to the coupling effect of external environmental noise and vibration noise, the feature rate of the original hydroelectric unit fault signal is not prominent, which will affect the performance of fault diagnosis algorithms. To solve the above problems, this paper proposes a PSO-MCKD-MFFResnet algorithm for fault diagnosis of hydropower units (Particle swarm optimization, PSO; Maximum correlation kurtosis deconvolution, MCKD; Multi-scale feature fusion residual network, MFFResnet). In practical applications, the selection of key parameters in the traditional MCKD method is heavily dependent on prior knowledge. First, this paper proposes a PSO-MCKD enhancement algorithm for fault features, which uses the PSO algorithm to search for the influencing parameters of MCKD to enhance the features from the original fault signal. Second, a fault feature diagnosis algorithm based on MFFResnet is proposed to improve the utilization of local features. The multi-scale residual module is used to extract features at different scales and then put the enhanced signal into MFFResnet for training and classification. The experimental results show that our approach can accurately and effectively classify the fault types of hydropower units, with an accuracy rate of 98.85$ % $. It is superior to other representative algorithms in different indicators and has a good stability.

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