Novel Manifold Autoencoder for Industrial Process Fault Diagnosis

Fault diagnosis plays a pivotal role in ensuring the safety of industrial processes. In the realm of fault diagnosis, stack autoencoders (SAE) have gained widespread popularity for their robust nonlinear feature extraction. Nevertheless, the unsupervised training mechanism of SAE tends to capture in...

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Vydané v:IEEE transactions on industrial informatics Ročník 21; číslo 1; s. 858 - 865
Hlavní autori: He, Yan-Lin, Lu, Zi-Yang, Zhu, Qun-Xiong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Autoencoder (AE)
Data mining
Data structures
Decoding
Fault diagnosis
Feature extraction
Informatics
Machine learning
Manifold learning
Manifolds
Manifolds (mathematics)
Mathematical models
Random forests
ISSN:1551-3203, 1941-0050
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Shrnutí:Fault diagnosis plays a pivotal role in ensuring the safety of industrial processes. In the realm of fault diagnosis, stack autoencoders (SAE) have gained widespread popularity for their robust nonlinear feature extraction. Nevertheless, the unsupervised training mechanism of SAE tends to capture information unrelated to the underlying data structure and data type. In response to this issue, this article introduces a novel approach called the manifold stack autoencoder (MSAE). Within the proposed MSAE framework, the feature extraction capabilities of SAE and the manifold learning abilities are functionally integrated. This innovative MSAE method effectively extracts both the data type and the manifold structure, thereby enhancing fault diagnosis accuracy. To assess the practicality and effectiveness of the proposed MSAE, simulations are carried out using the Tennessee Eastman dataset, employing a random forest classifier for fault classification. The simulation results conclusively demonstrate the outstanding performance of the MSAE in terms of fault diagnosis accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3465597