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

Author

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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)

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%.

Suggested Citation

  • Mingfei Hu & Xinyi Hu & Zhenzhou Deng & Bing Tu, 2022. "Fault Diagnosis of Tennessee Eastman Process with XGB-AVSSA-KELM Algorithm," Energies, MDPI, vol. 15(9), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3198-:d:803503
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    References listed on IDEAS

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    1. Chen, Chao & Reniers, Genserik & Khakzad, Nima, 2021. "A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Chen, Chao & Khakzad, Nima & Reniers, Genserik, 2020. "Dynamic vulnerability assessment of process plants with respect to vapor cloud explosions," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
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    1. Anping Wan & Qing Chang & Yinlong Zhang & Chao Wei & Reuben Seyram Komla Agbozo & Xiaoliang Zhao, 2022. "Optimal Load Distribution of CHP Based on Combined Deep Learning and Genetic Algorithm," Energies, MDPI, vol. 15(20), pages 1-19, October.

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