Abstract
A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.
Highlights
Fault has been a constant topic of research for several decades 1–4
The existing methods used to fault detection have been applied into a broad range of areas such as chemical process, Journal of Applied Mathematics networked control systems and semiconduction process, and so forth 8–11
The dynamic change, multiple mode, and nonlinearity exist objectively in the most of the aforementioned process, such as semiconduction process, tank reactors, and so forth 9–11, which has brought new challenges to the analysis and implementation of fault detection. They must be taken into account carefully in developing a high-performance and adaptive fault identification method to detect the abnormal cases as early as possible
Summary
Fault has been a constant topic of research for several decades 1–4. Several fault detection methods have been developed to solve problems since there exists a growing need for fault detection in the real process engineering from the plant’s safety perspective and from considering the quality of the process products 5–7. The existing methods used to fault detection have been applied into a broad range of areas such as chemical process, Journal of Applied Mathematics networked control systems and semiconduction process, and so forth 8–11. The dynamic change, multiple mode, and nonlinearity exist objectively in the most of the aforementioned process, such as semiconduction process, tank reactors, and so forth 9–11 , which has brought new challenges to the analysis and implementation of fault detection. They must be taken into account carefully in developing a high-performance and adaptive fault identification method to detect the abnormal cases as early as possible. Noted that data-based fault monitoring and identification methods were investigated in 11, 18, 19
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