A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

Abstract

Generally, the health conditions of rotating machinery are complicated and changeable. Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to automatically capture the useful fault labeled information from the monitoring raw vibration signals. That is to say, the intelligent unsupervised learning approach has a significant influence on fault diagnosis of rotating machinery. In this study, a span-new unsupervised deep learning network can be constructed based on the proposed feature extractor (L12 sparse filtering (L12SF)) and the designed clustering extractor (Weighted Euclidean Affinity Propagation) for resolving the issue that the acquisition of fault sample labeled information is burdensome, yet costly. Naturally, the novel intelligent fault diagnosis method of rotating machinery based on unsupervised deep learning network is first presented in this study. Thereinto, the proposed unsupervised deep learning network consists of two layers of unsupervised feature extractor (L12SF) and one layer of unsupervised clustering (Weighted Euclidean Affinity Propagation). L12SF can improve the regularization performance of sparse filtering, and Weighted Euclidean Affinity Propagation can get rid of the traditional Euclidean distance in affinity propagation that cannot highlight the contribution of different features in fault clustering. To make a long story short, the frequency spectrum signals are first entered into the constructed unsupervised deep learning network for fault feature representation; afterward, the unsupervised feature learning and unsupervised fault classification of rotating machinery can be implemented. The superiority of the proposed algorithms and method is validated by two cases of rolling bearing fault dataset. Ultimately, the proposed unsupervised fault diagnosis method can provide a theoretical basis for the development of intelligent unsupervised fault diagnosis technology for rotating machinery.

Fault Diagnosis in Rotating System Based on Vibration Analysis

“…Deep learning had formed a hot topic mostly in image application and object recognition. It is also suitable for large system with multiple variables and fault diagnosis [104][105][106][107].…”

A new fault diagnosis method based on adaptive spectrum mode extraction

“…4–6 In recent years, with the rapid development of rotating machinery toward complexity and intelligence, fault diagnosis of rolling bearings has become a huge challenge. 7 In particular, when the fault signal is disturbed by noise, how to effectively extract the fault features in the fault signal is the key to diagnose the fault.…”

Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

“…To target at this issue, various semi-supervised learning or unsupervised learning techniques have been proposed. [14][15][16][17] Data augmentation 18 and over-sampling 19 strategies are successfully used in the semi-supervised learning technique to enrich the labeled condition monitoring data. Clustering method 17 is also widely adopted in the unsupervised learning technique to evaluate the healthy mode of condition monitoring data without label information.…”

“…[14][15][16][17] Data augmentation 18 and over-sampling 19 strategies are successfully used in the semi-supervised learning technique to enrich the labeled condition monitoring data. Clustering method 17 is also widely adopted in the unsupervised learning technique to evaluate the healthy mode of condition monitoring data without label information. Besides, domain adaptation technique, as a separable branch of transfer learning, is also an effective approach to address such issue on account of that it is able to train a discriminative model using a source domain data set with rich label information and then transfer diagnostic knowledge into the target domain data set containing insufficient label information.…”