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A Segmental Autoencoder-based Fault Detection for Nonlinear Dynamic Systems: An Interpretable Learning Framework
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  • Hongtian Chen ,
  • Zhuofu Pan ,
  • Oguzhan Dogru ,
  • Yalin Wang ,
  • Biao Huang
Hongtian Chen
University of Alberta

Corresponding Author:[email protected]

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Zhuofu Pan
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Oguzhan Dogru
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Yalin Wang
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Biao Huang
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Abstract

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.