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

Research output: Contribution to journalJournal articlepeer-review

  • Min Xia
  • Teng Li
  • Lizhi Liu
  • Lin Xu
  • Clarence W. de Silva
<mark>Journal publication date</mark>7/09/2017
<mark>Journal</mark>IET Science, Measurement and Technology
Issue number6
Number of pages9
Pages (from-to)687-695
Publication StatusPublished
Early online date31/05/17
<mark>Original language</mark>English


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.