Adversarial Fault Detector Guided by One-Class Learning for a Multistage Centrifugal Pump

The data unavailability of critical machinery is an open issue in the field of condition-based maintenance research. Acquiring signals in all possible health conditions is impractical in most equipment. This lack of data affects the ability to extract informative features to build effective fault detectors; therefore, the development of proposals to deal with these conditions is necessary. To address this issue, we introduce a systematic methodology to build fault detection models from vibration signals for cyclo-stationary machines under limited data availability under faulty conditions. In the first step, vibration signals are modeled by unsupervised generative adversarial network (GAN)-based approach. Then, the best critic model for the GAN is determined for the feature extraction task guided by a one-class classifier. Finally, a fault detector is optimized to determine the health condition of the machinery. We propose an interpretation of the one-class support vector machine (SVM) hyperparameters for the feature space evaluation. The experiments carried out in the proposal were applied on a multistage centrifugal pump for single and multifault scenarios, which show a resulting feature space simpler than other methods reported in the literature, and outperform them in the fault detection task.