LAUSR

In real industrial scenarios, machines work in a healthy condition at most time. Thus, the number of healthy samples is far more than that of the fault ones. This results in the issue about data imbalance in machine fault diagnosis. In general, conducting multiple long-time failure tests can satisfy a sufficient and balanced dataset. However, it is impracticable due to massive costs. Aimed at all data-based fault diagnosis methods, data imbalance seriously affects their accuracy and robustness. Accordingly, a new method based on data augment is proposed to settle this problem. At first, a new GAN model named parallel classification Wasserstein generative adversarial network with gradient penalty (PCWGAN-GP) is designed. Then, healthy samples are input into PCWGAN-GP for generating more high-quality faulty samples to gradually augment the imbalanced dataset until it balances. At last, a fault diagnosis model is trained by the balanced dataset and applied to a testing set. For PCWGAN-GP, each faulty category independently equipped by a generator, a discriminator, and a classifier. In addition to the generation loss, discrimination loss, and classification loss, a Pearson loss function and a separability loss function are designed to improve PCWGAN-GP in sample generation for fault diagnosis. An experiment on a bearing dataset verifies the superiority of this proposed method in imbalanced fault diagnosis.