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Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named "FARM" for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM's unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we test and validate the performance of our FARM monitoring framework on Tennessee Eastman Process (TEP) benchmark dataset. We show that SPC achieves faster fault detection speed at a lower false alarm rate compared to state-of-the-art benchmark fault detection methods. In terms of fault classification diagnosis, we show that our modified SVM algorithm successfully classifies 17 out of 20 of the fault scenarios present in the TEP dataset. Compared with the results of standard SVM trained directly on the original dataset, our modified SVM improves the fault classification accuracy significantly.
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Fault Isolation for A Complex Decentralized Waste Water Treatment Facility
Summary Decentralized waste water treatment facilities monitor many features that are complexly related. The ability to detect the onset of a fault and to identify variables accurately that have shifted because of the fault are vital to maintaining proper system operation and high quality produced water. Various multivariate methods have been proposed to perform fault detection and isolation, but the methods require data to be independent and identically distributed when the process is in control, and most require a distributional assumption. We propose a distribution-free retrospective change-point-detection method for auto-correlated and non-stationary multivariate processes. We detrend the data by using observations from an in-control time period to account for expected changes due to external or user-controlled factors. Next, we perform the fused lasso, which penalizes differences in consecutive observations, to detect faults and to identify shifted variables. To account for auto-correlation, the regularization parameter is chosen by using an estimated effective sample size in the extended Bayesian information criterion. We demonstrate the performance of our method compared with a competitor in simulation. Finally, we apply our method to waste water treatment facility data with a known fault, and the variables identified by our proposed method are consistent with the operators’ diagnosis of the fault's cause.
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- Award ID(s):
- 1632227
- PAR ID:
- 10398880
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Volume:
- 69
- Issue:
- 4
- ISSN:
- 0035-9254
- Format(s):
- Medium: X Size: p. 931-951
- Size(s):
- p. 931-951
- Sponsoring Org:
- National Science Foundation
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