A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types

Rotating machinery can unexpectedly generate many new fault types under changing operating conditions. The capability of fault diagnostic models to adapt and acquire knowledge regarding new fault types is increasingly vital. However, traditional deep learning-based fault diagnosis models often encounter the challenge of catastrophic forgetting when facing new fault types. Numerous attempts were trapped with the stability–plasticity dilemma when tackling this phenomenon. In this study, we draw inspiration from the gradient boosting algorithm and propose a feature boosting based continual learning method. This method allows the diagnostic model to continuously and adaptively acquire knowledge of new fault types. Initially, the concept of gradient boosting is employed to construct an initial fault diagnostic model. Then, new modules are continuously extended dynamically for the initial diagnostic model to fit the residuals between the actual label and the output of the initial diagnostic model. Finally, to maintain the backbone of the fault diagnostic model as a single one, redundant parameters and feature dimensions are removed using an effective distillation strategy. Experimental results demonstrate that the feature boosting based continual learning method effectively mitigates catastrophic forgetting and enhances the plasticity of the fault diagnosis model, outperforming other existing methods.