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Chinese Journal of Mechanical Engineering
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ASCS online fault detection and isolation based on an improved MPCA

Chinese Journal of Mechanical Engineering volume 27, pages 1047–1056 (2014)Cite this article

Abstract

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

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Authors and Affiliations

  1. Science and Technology on Vehicle Transmission Laboratory, Beijing Institute of Technology, Beijing, 100081, China

    Jianxin Peng, Haiou Liu, Yuhui Hu, Junqiang Xi & Huiyan Chen

  2. The State Key Laboratory of the Vehicle Transmission, China North Vehicle Research Institute, Beijing, 100072, China

    Jianxin Peng

Authors
  1. Jianxin Peng
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  2. Haiou Liu
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  3. Yuhui Hu
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  4. Junqiang Xi
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  5. Huiyan Chen
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Corresponding author

Correspondence to Haiou Liu.

Additional information

Supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2011AA11A223)

PENG Jianxin, born in 1986, is currently a PhD candidate at Science and Technology on Vehicle Transmission Laboratory, Beijing Institute of Technology, China. His research interests include vehicle transmission system control and fault diagnosis.

LIU Haiou, born in 1975, is currently an associate professor at Science and Technology on Vehicle Transmission Laboratory, Beijing Institute of Technology, China. Her research interests include vehicle transmission system control and fault diagnosis.

CHEN Huiyan, born in 1961, is currently professor at Science and Technology on Vehicle Transmission Laboratory, Beijing Institute of Technology, China. His research interest is intelligent vehicle system.

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Peng, J., Liu, H., Hu, Y. et al. ASCS online fault detection and isolation based on an improved MPCA. Chin. J. Mech. Eng. 27, 1047–1056 (2014). https://doi.org/10.3901/CJME.2014.0529.106

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  • DOI: https://doi.org/10.3901/CJME.2014.0529.106

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