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• W2891605032 abstract "Real-time process monitoring is crucial to improve the productivity, process safety, and product quality. In this paper, a novel fault detection and diagnosis technique based on a principal polynomial analysis (PPA) is proposed. PPA is a nonlinear modeling technique, which describes the data using a set of flexible principal polynomial components. Compared with the PCA-based methods, PPA is more effective in capturing the intrinsic nonlinear geometry structure of the process data. Moreover, compared with other nonlinear methods, such as kernel-based and neural-networks-based methods, PPA has the appealing features of straightforward out-of-sample extension, volume-preservation, and invertibility. In addition, two new types of fault detection and diagnosis statistics are derived. The effectiveness of the proposed PPA-based monitoring method was verified through its applications to a nonlinear numerical example and an industrial benchmark process. The application results have demonstrated that the proposed method has superior fault detection and diagnosis performance than the conventional PCA-based and kernel PCA-based methods." @default.
• W2891605032 created "2018-09-27" @default.
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• W2891605032 date "2018-01-01" @default.
• W2891605032 modified "2023-10-01" @default.
• W2891605032 title "Principal Polynomial Analysis for Fault Detection and Diagnosis of Industrial Processes" @default.
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• W2891605032 doi "https://doi.org/10.1109/access.2018.2870140" @default.
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