Fault Diagnosis of Tennessee Eastman Process with XGB-AVSSA-KELM Algorithm

Mingfei Hu, Xinyi Hu, Zhenzhou Deng and Bing Tu
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Mingfei Hu: Qianhu College, Nanchang University, Nanchang 330031, China
Xinyi Hu: Qianhu College, Nanchang University, Nanchang 330031, China
Zhenzhou Deng: Information Engineering College, Nanchang University, Nanchang 330031, China
Bing Tu: Chongqing Research Institute, Nanchang University, Chongqing 402660, China

Energies, 2022, vol. 15, issue 9, 1-25

Abstract: In fault detection and the diagnosis of large industrial systems, whose chemical processes usually exhibit complex, high-dimensional, time-varying and non-Gaussian characteristics, the classification accuracy of traditional methods is low. In this paper, a kernel limit learning machine (KELM) based on an adaptive variation sparrow search algorithm (AVSSA) is proposed. Firstly, the dataset is optimized by removing redundant features using the eXtreme Gradient Boosting (XGBOOST) model. Secondly, a new optimization algorithm, AVSSA, is proposed to automatically adjust the network hyperparameters of KELM to improve the performance of the fault classifier. Finally, the optimized feature sequences are fed into the proposed classifier to obtain the final diagnosis results. The Tennessee Eastman (TE) chemical process is used to verify the effectiveness of the proposed method through multidimensional diagnostic metrics. The results show that our proposed diagnosis method can significantly improve the accuracy of TE process fault diagnosis compared with traditional optimization algorithms. The average diagnosis rate for 21 faults was 91.00%.

Keywords: fault diagnosis; Tennessee Eastman process; KELM; XGBOOST; AVSSA; feature selection (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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