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Volume 8 Issue 8
Aug.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Donglei Zheng, Le Zhou and Zhihuan Song, "Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process," IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1465-1476, Aug. 2021. doi: 10.1109/JAS.2021.1004090
Citation: Donglei Zheng, Le Zhou and Zhihuan Song, "Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process," IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1465-1476, Aug. 2021. doi: 10.1109/JAS.2021.1004090

Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

doi: 10.1109/JAS.2021.1004090
Funds:  This work was supported by Zhejiang Provincial Natural Science Foundation of China (LY19F030003), Key Research and Development Project of Zhejiang Province (2021C04030), the National Natural Science Foundation of China (62003306) and Educational Commission Research Program of Zhejiang Province (Y202044842)
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  • In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process monitoring strategy, all these multi-rate measurements should be considered for data-based modeling and monitoring. In this paper, a novel kernel multi-rate probabilistic principal component analysis (K-MPPCA) model is proposed to extract the nonlinear correlations among different sampling rates. In the proposed model, the model parameters are calibrated using the kernel trick and the expectation-maximum (EM) algorithm. Also, the corresponding fault detection methods based on the nonlinear features are developed. Finally, a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.


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    • The constraint nonlinear relationships with different sampling rates are considered in the proposed model.
    • A nonlinear EM algorithm is proposed for model parameter estimation.
    • A fault detection scheme for a nonlinear multi-rate process is developed.


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