CGAN-MBL for Reliability Assessment With Imbalanced Transmission Gear Data

In term of data-driven techniques for reliability assessment of transmission gears, it is necessary to collect sufficient data of each class in practice. However, the fact that highest and lowest reliability events rarely occur would result in a class imbalance problem. To resolve such an issue, we propose conditional generative adversarial network-mean-covariance balancing labeling (CGAN-MBL) to improve reliability assessment of transmission gears with insufficient and imbalanced data. First, we establish a CGAN-based model to generate credible instances with a similar distribution of original data. In the model, it is proven that by introducing a network weight initialization scheme, the ability to escape from local optimum is enhanced and global search ability of CGAN is strengthened; minibatch discriminator is designed to decrease mode collapse by distinguishing a batch of original and generated data iteratively. Second, in order to overcome the drawback of CGAN with unlabeled generated data, MBL is introduced to tag the generated data through exploring the nearest distance between generated instances and original class centers. Finally, synthetic data combined generated data with original ones train classifiers. Simulations show the effectiveness of the proposed approach for imbalance learning in real-world transmission gear data.