Semi-Supervised Adversarial Transfer Networks for Cross-Domain Intelligent Fault Diagnosis of Rolling Bearings  
[PERSON]  
1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China 12 Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China 2  
[PERSON]  
1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China 12 Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China 2  
[PERSON]  
1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China 12 Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China 2  
[PERSON]  
1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China 12 Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China 2  
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distribution, which leads to the deterioration of cross-domain diagnosis performance. Such challenging issues that attempt to transfer learned features from source domain to target domain are called the domain shift problem [10]. Specifically, the source-domain classifier can only utilize training data from the source domain to effectively train. However, the performance of model is degraded owing to the domain shift, which makes the diagnosis task unattainable. Figure 1 shows the domain shift phenomenon. In order to address the above problems, scholars have developed the domain adaptation (DA) method for diminishing sample distribution discrepancy [11]. Concretely, DA can utilize relevant domain-invariant features extracted from the domain to accomplish a diagnosis task. Meanwhile, many recent studies have shown that DA exhibits encouraging performance on cross-domain works, including semantic correlation transfer [12], handwritten text recognition [13], speaker recognition [14], etc. Although DA is effective in solving cross-domain problems, most of the existing methods assume that source-domain labels are available for each health state, i.e., all source domain data should be labeled [15; 16; 17; 18]. However, this assumption is almost unpractical in real-world applications. On the one hand, for large rotating machinery, it is impractical to perform massive and detailed full-cycle testing in actual field, because a lot of time must be spent to obtain reliable fault status signals. On the other hand, due to the complexity and uncertainty of mechanical systems, even if vibration signals can be collected in advance, most of the signal labels are unknown. Consequently, source domain labels are insufficient for domain adaptation, resulting in inefficient detection performance. Fortunately, a small number of labeled signals are easily available, and they can be applied to adaptive fault detection. To overcome the above drawbacks, a semi-supervised adversarial transfer network for cross-domain diagnosis is proposed in this article. Different from the assumption of most existing research, the proposed method does not require a sufficient number of source domain labels for network training. First, a signal-to-image method is adopted to convert vibration signals to images by employing the Gramian Angular Field (GAF). Next, a generative label extension module is designed to generate artificial labels to address the shortage of source domain labels.