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A Segmental Autoencoder-based Fault Detection for Nonlinear Dynamic Systems: An Interpretable Learning Framework

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posted on 2022-02-01, 03:00 authored by Hongtian ChenHongtian Chen, Zhuofu Pan, Oguzhan Dogru, Yalin Wang, Biao Huang
This paper presents a segmental autoencoder-based fault detection (FD) framework for nonlinear dynamic systems. The basic idea behind the proposed FD scheme is to identify a generalized kernel representation based on the representation knowledge learned from an autoencoder. By using the system data, several cascades, linking nonlinear operators, are employed to obtain a data-based model which describes the nonlinear dynamic behaviors. With the help of the segmental structure of an autoencoder, a residual generator is then constructed. Rigorous mathematical analysis and an application on a continuous stirred tank reactor demonstrate the effectiveness of the proposed FD method.


Natural Sciences and Engineering Research Council of Canada


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University of Alberta

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  • Canada

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