Pattern modeling and fault detection based on dynamic controlled autoencoder

Industrial processes often exhibit significant nonlinear and dynamic characteristics. To effectively monitor these processes, this paper proposes a dynamic controlled autoencoder (DCAE) model for pattern extraction, which primarily consists of an autoencoder and dynamic mapping components. It is cap...

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Veröffentlicht in:Chemometrics and intelligent laboratory systems Jg. 263; S. 105422
Hauptverfasser: Guo, Wei, Luan, Xiaoli, Liu, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.08.2025
Schlagworte:
Dynamic causality
Dynamic controlled autoencoder
Fault detection
Nonlinear process
Pattern extraction
Fault detection
Dynamic controlled autoencoder
Dynamic causality
Pattern extraction
Nonlinear process
ISSN:0169-7439
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Zusammenfassung:Industrial processes often exhibit significant nonlinear and dynamic characteristics. To effectively monitor these processes, this paper proposes a dynamic controlled autoencoder (DCAE) model for pattern extraction, which primarily consists of an autoencoder and dynamic mapping components. It is capable of simultaneously extracting the nonlinear structural relationships of process variables in static space and their nonlinear dynamics in the time domain, and in particular, establishing the dynamic causality between control input and pattern. The dynamic controlled pattern extracted using DCAE can sufficiently represent the operation information of the nonlinear process. Then, the relationships between DCAE modeling errors and model variables are explored, leading to the construction of error statistics for monitoring industrial processes and the development of a DCAE-based fault detection scheme. Finally, the case study of an industrial boiler combustion system illustrates the effectiveness and superiority of the DCAE model in extracting the pattern of industrial processes and performing fault detection. •A dynamic controlled autoencoder model is proposed for pattern modeling of nonlinear dynamic processes.•Several modeling errors of the proposed model are analyzed, revealing its role as a dynamic whitening filter.•The effectiveness of the proposed model in pattern extraction and fault detection is verified by an industrial case.
ISSN:0169-7439
DOI:10.1016/j.chemolab.2025.105422