LAUSR

Abstract Diagnosis of rolling bearings plays an important role in condition monitoring of industrial rotating machinery. In many actual applications, rolling bearings work in normal state at most time and faulty samples are difficult to be collected. Thus, it is easy to arise problem of imbalanced dataset which restricts accuracy and stability of fault diagnosis. Generative adversarial networks (GANs) have been proved to be effective to produce artificial data that are alike real data, and have been widely used in image fields. Data synthesis using deep generative model provide a promising methodology for imbalanced fault diagnosis of machinery. In this paper, we propose a novel framework named dual discriminator conditional generative adversarial networks (D2CGANs) to learn from sensor signals on multimodal fault samples and automatically synthesize realistic one-dimensional signals of each fault. The framework is designed to produce realistic multimodal samples with fault labels and dual-discriminator structure is benefit to enhance the quality and diversity of synthesized data without mode collapse. Then, synthesized data can be used for data augmentation to improve the accuracy of imbalanced fault diagnosis. In order to evaluate the performance of the generative model, we introduce a set of assessments to evaluate quality and diversity of synthesized data, including quantitative statistical metrics and qualitative visualization. Finally, experiments on rolling bearings datasets from Case Western Reserve University (CWRU) are implemented to verify the effectiveness of the proposed approach for imbalanced fault diagnosis. Results demonstrate our method outperforms other widely used synthesis techniques in terms of data synthesis quality and fault diagnosis accuracy, and timeliness analysis also denotes our method can meet requirement of online fault diagnosis.