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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(3): 764-775
Jinchuan Qian, Li Jiang and Zhihuan Song, "Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 764-775, May 2020. doi: 10.1109/JAS.2020.1003147
Citation: Jinchuan Qian, Li Jiang and Zhihuan Song, "Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 764-775, May 2020. doi: 10.1109/JAS.2020.1003147
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Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

doi: 10.1109/JAS.2020.1003147

  • This work was supported by the Key Project of Natural Science Foundation of China ( 61933013) and Ningbo 13th five-year marine economic innovation and development demonstration project NBHY-2017-Z1. Corresponding Author (Zhihuan Song)

Funds:  This work was supported by the Key Project of National Natural Science Foundation of China (61933013) and Ningbo 13th Five-year Marine Economic Innovation and Development Demonstration Project (NBH Y-2017-Z1)
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    Abstract
  • This paper proposes a novel locally linear back-propagation based contribution (LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder (AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution (RBC), the propagation of fault information is described by using back-propagation (BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well, and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

     

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    Highlights

    • Improving the reconstructed contribution based fault diagnosis for auto-encoder.
    • Describing the propagation of the fault by the back-propagation algorithm.
    • Building the locally linear model for online fault diagnosis.
    • Proposing a special contribution index for suppressing the smearing effect.

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