Non-revisiting genetic cost-sensitive sparse autoencoder for imbalanced fault diagnosis

YY It is hard to obtain sufficient fault samples in most real-world industrial scenarios. This has raised the need of addressing the critical issue of imbalanced fault diagnosis that remains a major challenge for popular fault diagnosis methods such as the autoencoder(AE). In this research, we propose non-revisiting genetic cost-sensitive sparse autoencoder(NrGCS-SAE) solution, which not only incor-porates cost-sensitive learning with sparse autoencoder but also solves the problem of class weights assignment. Specifically, sparse autoencoder is adopted as it has better generalization performance than autoencoder, and genetic algorithm(GA) is employed to optimize class weights that are initially unknown. In addition, a non-revisiting strategy is devised to prevent repeated evaluation of the same individual in different generations, which can help increase exploration ability and decrease computing costs. Computational experiments are used to evaluate the proposed NrGCS-SAE solution on the Tennessee Eastman(TE) dataset and the real plasma etching process dataset, which involves both binary imbalanced fault diagnosis and multi-class imbalanced faults diagnosis. As evidenced in the tests, NrGCS-SAE achieves improved performance and more importantly this improvement is consistent in different settings of experiments. (C) 2021 Elsevier B.V. All rights reserved.