Unsupervised incremental transfer learning with knowledge distillation for online remaining useful life prediction of rotating machinery
Yingqin Liang, Wentao Mao, Chao-Szu Wu
Abstract
Online remaining useful life (RUL) prediction has been solved by deep transfer learning, but is still with challenges as follows: (1) Incomplete and unlabeled data under actual operation; (2) Condition monitoring data is streaming with unknown distribution; and (3) The distribution of degradation data is variable. To solve them, an unsupervised incremental transfer learning approach with knowledge distillation (KD) is proposed. First, a time series recursive prediction model is built to generate pseudo labels. Second, an online KD network is constructed to realize unsupervised domain adaptation. Finally, an incremental updating mechanism is designed in the online KD network for online RUL prediction with the pseudo labels. Comparative experiments on the IEEE PHM Challenge 2012 rolling bearing dataset show that the proposed method, being computationally inexpensive, can effectively achieve dynamic prediction only with sequentially-collected data, which can provide an effective RUL prediction solution for rotating machinery under an open environment.