In industrial settings, it is important to diagnose new faults from the updated data stream. The model that learns incrementally by taking in new data can meet this requirement. However, the problems of time-consuming iterative updates and catastrophic forgetting in traditional incremental learning methods greatly reduce the diagnostic efficiency. To solve these problems, this paper introduces a replay-free evolutionary classifier (RFEC) architecture. The core of this strategy is to optimize the classification decision process by reconstructing the association between classification weights and feature vectors, with effective constraints on the objective function. Also, a nonparametric prototype classifier is introduced to ensure timely online fault diagnosis. This classifier uses classification prototypes and initial weights to allow quick updates during later incremental phases. The experimental results of the wind turbine simulation experimental platform show that RFEC has better accuracy and real-time update efficiency than other popular incremental learning methods. Specifically, it achieves an average accuracy of 90.57% and a dramatically reduced total training time of approximately 0.06 seconds during the incremental update stage.
He et al. (Wed,) studied this question.