Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowle...

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Veröffentlicht in:IET science, measurement & technology Jg. 11; H. 6; S. 687 - 695
Hauptverfasser: Xia, Min, Li, Teng, Liu, Lizhi, Xu, Lin, de Silva, Clarence W
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.09.2017
Schlagworte:
condition monitoring
data‐driven approach
deep neural network
encoding
fault classification
fault diagnosis
feature extraction
intelligent fault diagnosis approach
neural nets
operational safety
representative feature extraction
Research Article
signal classification
signal denoising
signal representation
stacked denoising autoencoder
supervised artificial intelligence algorithms
trained DNN model
unsupervised feature learning
unsupervised learning
stacked denoising autoencoder
fault diagnosis
supervised artificial intelligence algorithms
intelligent fault diagnosis approach
operational safety
encoding
unsupervised feature learning
representative feature extraction
signal representation
unsupervised learning
signal classification
signal denoising
fault classification
feature extraction
data-driven approach
trained DNN model
condition monitoring
neural nets
deep neural network
ISSN:1751-8822, 1751-8830
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Beschreibung
Zusammenfassung:Condition monitoring and fault diagnosis are important for maintaining the system performance and guaranteeing the operational safety. The traditional data-driven approaches mostly incorporate well-defined features and methodologies such as supervised artificial intelligence algorithms. Prior knowledge of possible features and a large quantity of labelled condition data are needed. Besides, many traditional approaches require rebuilding or a retraining of the original model to diagnosis new conditions. The present study proposes an intelligent fault diagnosis approach that uses a deep neural network (DNN) based on stacked denoising autoencoder. Representative features are learned by applying the denoising autoencoder to the unlabelled data in an unsupervised manner. A DNN is then constructed and fine-tuned with just a few items of labelled data. The trained DNN achieves high performance in fault classification. Furthermore, new conditions can be correctly classified by simply fine-tuning the trained DNN model using a small amount of labelled data under the new conditions. The effectiveness of the proposed approach is evaluated using a case study of fault diagnosis of a bearing unit. The results indicate that the proposed method can extract representative features from massive unlabelled data on the system condition and achieve high performance in fault diagnosis.
ISSN:1751-8822
1751-8830
DOI:10.1049/iet-smt.2016.0423