Bayesian Deep-Learning-Based Prognostic Model for Equipment Without Label Data Related to Lifetime

Abstract

Deep learning has become a promising tool for processing the massive data and attracted an increasing attention in the fields of degradation modeling and remaining useful life (RUL) prediction. The existing deep-learning-based methods are generally faced with the two aspects of problems. On the one hand, the prediction results are represented by the point estimates instead of the probabilistic distribution and, thus, the prognostic uncertainty in RUL prediction cannot be characterized. On the other hand, there exist plenty of the engineering assets without the label data related to lifetime, posing a great challenge for training the deep learning network. Toward this end, we propose a prognostic model under the framework of Bayesian deep learning for equipment lacking the label data related to lifetime. First, the monitoring data of the historical equipment and the historical data of field equipment in the database are preprocessed to generate the samples regarding degradation information as a label. Second, the bidirectional recurrent neural network (RNN) is employed as the candidate network for the advantages in handling the sequential monitoring data. On the basis of this, the idea of Bayesian deep learning is incorporated into the bidirectional RNN; thus, we can characterize the uncertainty of the predicted degradation level at any future time via utilizing the variational inference technique in the Bayesian neural networks. Furthermore, the failure probability for the concerned equipment at any time can be determined, by which the degradation uncertainty can be converted into the RUL uncertainty from the point of the reliability theory. Finally, we provide the case study associated with lithium-ion batteries to verify the proposed prognostic model.