Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation

Abstract

<jats:p>Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.</jats:p>

Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing

“…Domain adversarial neural network (DANN): This method was first proposed by Ganin et al [ 53 ] and used in several studies [ 42 , 43 ]. In this method, a discriminator is added, and the features of the source domain and the target domain are not known.…”

“…Kim et al proposed repurposing method for parameter transfer [ 41 ]. Third, CNN with an adversarial concept was actively performed [ 42 , 43 ]. Furthermore, Zhao et al conducted a study to implement and compare the various models and provided the implemented source [ 44 ].…”

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

“…Within the field of DA, there are two main types of approaches: semi-supervised and unsupervised. The major difference between the two types of DA is that semi-supervised DA requires a limited number of the target data to be labelled [ 40 , 41 , 42 , 43 ]. Unsupervised DA, on the other hand, does not need any observation from the target data to be labelled [ 44 , 45 , 46 , 47 ].…”

A hybrid Chinese word segmentation model for quality management-related texts based on transfer learning

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