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 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.