Abstract: Intelligent fault diagnosis methods play an important role in ensuring the equipment safely and reliably operating. However, they cannot effectively classify the discontinuous time-varying sample space, for most of them belong to batch learning and lack continual learning ability. A continual learning fault diagnosis method for discontinuous time-varying sample space based on the artificial immune system, CLFDMDTVS, is proposed. It is inspired by the intelligent mechanism that memory cells of the biological immune system can recognize previous invaders when they attack again very fast and more efficiently, and these memory cells can evolve with the evolution of previous invaders. CLFDMDTVS will degenerate into a common supervised learning fault diagnosis method when all data independent of time. Memory cells were continuously updated by learning testing data during the fault diagnosis stage, the samples which are not used to train and the reappeared samples which are discontinuous can be diagnosed by affinity threshold. To assess the performance and possible advantages of CLFDMDTVS, the experiments on 20 well-known datasets from the UCI repository and XJTU-SY rolling element bearing accelerated life test datasets were performed. Results show that CLFDMDTVS has better fault diagnosis performance for time-invariant data, and outperforms the other methods for the problems with discontinuous time-varying sample space.

Key words: time-varying sample, continual learning, fault diagnosis, biological immune mechanism, artificial immune algorithm, rolling element bearing