Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.

Abstract

The paper aims to develop a joint learning method for failure mode recognition and RUL prediction of operating units. Specifically, the developed method addresses a challenging issue in practice, i.e., how to effectively conduct failure mode recognition and RUL prediction as a joint task based on interpretable extracted degradation features from multiple sensor signals. To implement this method in practice, four steps are included as follows: First, collect multiple sensor signals, failure time, and failure modes of historical units. Second, construct the joint learning model based on features extracted from sensor signals by considering the degradation mechanism. Third, estimate model parameters using the data of historical units. Fourth, recognize the failure mode and predict the RUL of an in-service unit. Since the proposed method is a data-driven neural network with flexible model structure that considers complex data relationships, it is expected to be applicable to many practical situations and use cases, especially for manufacturing systems with complex structures and unknown failure thresholds.