A co-training-based approach for prediction of remaining useful life utilizing both failure and suspension data

Abstract

Traditional data-driven prognostics often requires some amount of failure data for the offline training in order to achieve good accuracy for the online prediction. Failure data refer to condition monitoring data collected from the very beginning of an engineered system's lifetime till the occurrence of its failure. However, in many engineered systems, failure data are fairly expensive and time-consuming to obtain while suspension data are readily available. Suspension data refer to condition monitoring data acquired from the very beginning of an engineered system's lifetime till planned inspection or maintenance when the system is taken out of service. In such cases, it becomes essentially critical to utilize suspension data which may carry rich information regarding the degradation trend and help achieve more accurate remaining useful life (RUL) prediction. To this end, this paper proposes a co-training-based data-driven prognostic approach, denoted by COPROG, which uses two data-driven algorithms with each predicting RULs of suspension units for the other. After a suspension unit is chosen and its RUL is predicted by an individual algorithm, it becomes a virtual failure unit that is added to the training data set of the other individual algorithm. Results obtained from two case studies suggest that COPROG gives more accurate RUL prediction, as compared to any individual algorithm with no use of suspension data, and that COPROG can effectively exploit suspension data to improve the prognostic accuracy.