Online fault detection for industrial processes through Kalman filter



Published Sep 4, 2023
Wenyi Liu Takehisa Yairi


Industrial processes suffer from a wide range of damages including normal wear, environmental changes, physical structural defects and so on. This paper describes the possibility of system health management based on a prediction model, i.e., state space model realized by Kalman filter. The categorical target was mapped to numerical values in advance for this purpose. To deal with the time-varying and streaming characteristics of the industrial process, the model is applied in an online fashion. Comparing with conventional fault detection techniques, this model has the advantages of monitoring not only the production process of interests through observation equation, but also the structural anomalies described via unseen states estimation. In addition, the process and measurement noises provide valuable information about the unstructured uncertainties caused by other reasons. Experiments have been conducted to valid the effectiveness of the proposed method.

Abstract 157 | PDF Downloads 144



dynamic process, Kalman filter, fault detection

Bishop, C. M., & Nasrabadi, N. M. (2006). Sequential data. In Pattern recognition and machine learning (p. 642–643). New York, USA: Springer New York.

Blazquez-Garcıa, A., Conde, A., Mori, U., & Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys, 54(3).

Braei, M., & Wagner, D.-I. S. (2020). Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv:2004.00433 [cs.LG].

Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(15), 1-58.

Fatehi, A., & Huang, B. (2017). Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay. Journal of Process Control, 53, 15–25.

Favoreel, W., De Moor, B., & Overschee, P. V. (2000). Subspace state space system identification for industrial processes. Journal of Process Control, 10(2-3), 149–155.

Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products. Chemometrics and intelligent laboratory systems, 83(2), 83-90.

Kadlec, P., Grbic, R., & Gabrys, B. (2011). Review of adaptation mechanisms for data-driven soft sensors. Computers and Chemical Engineering, 35(1), 1-24.

Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the kalman filter. The American Statistician, 37(2), 123-127.

Platt, J. (1999). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3), 61- 74.

Zhang, R., Xue, A., & Gao, F. (2014). Temperature control of industrial coke furnace using novel state space model predictive control. IEEE Transactions on Industrial Informatics, 10(4), 2084–2092.
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