Bayesian deep-learning for RUL prediction: An active learning perspective

Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction in the recent decade. Although with high precision and flexibility, DL methods need sufficient run-to-failure data to guarantee their performance. However, run-to-failure data is fairly expensive to obtain in many industrial applications. How to economically achieve high accuracy with few run-to-failure data becomes a critical and emergent issue. In this study, a Bayesian deep-active-learning framework is proposed for RUL prediction, which goes beyond traditional passive learning and introduces a novel active learning perspective. We use Bayesian neural networks with Monte Carlo dropout inference to predict RUL with uncertainty quantification for samples without run-to-failure labels. The prediction uncertainty is further used to develop an acquisition function for actively selecting target samples to obtain their run-to-failure labels. A recursive model training and active data selection mechanism are then developed to maintain accuracy while reducing the size of the training data. Two practical examples, one from a public bearing dataset and the other from our lab testing on battery degradation, are presented to demonstrate the proposed method. Experimental results demonstrate that 20 and 40% of run-to-failure data can be saved for the bearing and the battery RUL prediction, respectively.