Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Hui Zhang, Cunhua Pan, Yuanxin Wang, Min Xu, Fu Zhou, Xin Yang, Lou Zhu, Chao Zhao, Yangfan Song and Hongwei Chen
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Hui Zhang: Datang East China Electric Power Test & Research Institute, Hefei 230000, China
Cunhua Pan: Datang East China Electric Power Test & Research Institute, Hefei 230000, China
Yuanxin Wang: Datang East China Electric Power Test & Research Institute, Hefei 230000, China
Min Xu: Datang East China Electric Power Test & Research Institute, Hefei 230000, China
Fu Zhou: Datang East China Electric Power Test & Research Institute, Hefei 230000, China
Xin Yang: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Baoding 071003, China
Lou Zhu: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Baoding 071003, China
Chao Zhao: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Baoding 071003, China
Yangfan Song: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Baoding 071003, China
Hongwei Chen: Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Baoding 071003, China

Energies, 2022, vol. 15, issue 15, 1-14

Abstract: Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis.

Keywords: variational model decomposition; kernel principal component analysis; kernel extreme learning machine; coal mill; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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