Long-Tail Class Incremental Learning via Bias Calibration With Application to Continuous Fault Diagnosis

Class incremental learning (CIL) offers a promising framework for continuous fault diagnosis (CFD), allowing networks to accumulate knowledge from streaming industrial data and recognize new fault classes. However, current CIL methods assume a balanced data stream, which does not align with the long-tail distribution of fault classes in real industrial scenarios. To fill this gap, this article investigates the impact of long-tail bias in the data stream on the CIL training process through the experimental analysis. Observations show that long-tail bias in the data stream has a cascading effect, affecting the retention of old task knowledge and learning new tasks. Concurrently, the incremental model encounters challenges in identifying samples that conflict with its biases. Accordingly, we propose a CFD method called long-tail CIL via bias calibration (LTCIL-BC), which aims to improve the learning of bias-conflicting samples through bias exploration and debiasing. Specifically, LTCIL-BC simultaneously trains a primary debiased network and an auxiliary biased network. Then, a bias-indicating score is developed to provide insight into model bias and data bias based on the prediction error of the primary and auxiliary models, respectively. LTCIL-BC subsequently adjusts the logits of the debiased network using the bias-indicating score to guide optimization, thereby better utilizing the role of old class exemplars and reducing catastrophic forgetting. Experiments on power system (PS) and secure water treatment (SWaT) datasets demonstrate the superior performance of LTCIL-BC in CFD, achieving up to 9% improvement over state-of-the-art baselines in multiple long-tailed CIL setting. Comprehensive results demonstrate the effectiveness of LTCIL-BC in jointly addressing data and model bias during calibration and prioritizing bias-conflicting samples.