- Tytuł:
- A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
- Autorzy:
-
Huang, Lei
Ren, Hao
Chai, Yi
Qu, Jianfeng - Powiązania:
- https://bibliotekanauki.pl/articles/1838184.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
fault detection
sparse autoencoder
sparse restricted Boltzmann machine
hybrid industrial processes
detekcja błędu
autoenkoder
proces przemysłowy - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 29-43
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł