As a vital technology for ensuring the stable operation of industrial equipment, fault diagnosis has received a lot of research in recent years. Most complex industrial processes are in normal working conditions during operation, so the amount of data collected under normal working conditions is much larger than that under fault working conditions. The uneven number of samples will lead to the imbalance of datasets and make it a challenging task to assure the overall accuracy. To address the issue, an innovative imbalanced fault diagnostic approach based on area identification conditional generative adversarial networks (AICGAN) is proposed. First, considering the imbalance between normal data (majority data) and fault data (minority data), a hybrid data generation method combining over-sampling and AICGAN generator is proposed, which effectively extends the limited minority data and overcomes the inclination to majority data to some extent. On one hand, the over-sampling algorithm reduces the impact of dataset imbalance on the AICGAN training process by linear interpolation. On the other hand, the trainable generator can create samples similar to real samples by learning the generation principle so as to enrich the minority data information and reduce the sample stacking caused by linear synthesis. The two sample production methods complement each other. Combining the raw samples, over-sampled samples, and samples generated by generator, a new dataset is constructed. Second, the new dataset is used to train the AICGAN discriminator. In addition, in order to generate samples with higher value, an auxiliary discrimination layer is added to the discriminator to control the pattern of generated samples. Third, the balanced dataset containing the linear synthesis samples and the samples generated by the trained generator are put into the classifier to obtain the fault diagnosis. The effectiveness of the proposed approach for fault diagnosis based on AICGAN is verified using the three-phase flow facility (TFF) dataset and the Tennessee Eastman (TE) dataset. The experimental results demonstrate that the AICGAN-based fault diagnosis method achieves high F1 scores on the imbalanced dataset.
- fault diagnosis
- generative adversarial networks
- imbalanced dataset