<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.</p>
</abstract>

This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid industrial processes.

A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes

Key Laboratory of Complex System Safety and Control Ministry of Education

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid industrial processes. The method is...

A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes

Online veröffentlicht: 03. Apr. 2021

Seitenbereich: 29 - 43

Eingereicht: 30. Jan. 2020

Akzeptiert: 02. Nov. 2020

DOI: https://doi.org/10.34768/amcs-2021-0003

Schlüsselwörter
fault detection, sparse auto-encoder, sparse restricted Boltzmann machine, hybrid industrial processes

© 2021 Lei Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes

Online veröffentlicht: 03. Apr. 2021

Seitenbereich: 29 - 43

Eingereicht: 30. Jan. 2020

Akzeptiert: 02. Nov. 2020

DOI: https://doi.org/10.34768/amcs-2021-0003

Schlüsselwörterfault detection, sparse auto-encoder, sparse restricted Boltzmann machine, hybrid industrial processes

© 2021 Lei Huang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
fault detection, sparse auto-encoder, sparse restricted Boltzmann machine, hybrid industrial processes