Discriminant Generative Adversarial Networks With Its Application To Equipment Health Classification

In equipment health classification, machines in normal, degradation and critical stages are classified based on domain experts KPI (Remaining Useful Life). Higher KPI values indicate healthier machines. GANs can be used to generate sensor data for machines in different health stages. There are challenges for this type of sensor generation. Firstly, the generated samples for different health stages should be well separated. For example, it is not preferred that generated samples in critical stage have higher KPI values than generated samples in degradation stage. Secondly, sensor data in different stages are not equally different with each other. For instance, sensor data in normal stage is more like sensor data in degradation stage than that in critical stage. However, in existing GAN, data labels are represented using one-hot vectors and different between-class distances are not explicitly considered. We propose discriminant GANs, where, for generated samples, we maximize between-class distance and minimize within-class distance, so that generated samples in different classes are more separable and different between-class distances are explicitly allowed. Empirical experiments show that (1) discriminant regularization improves the quality of generated samples, (2) discriminant regularized GANs extract efficient features for equipment health classification.