Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. However, under the realm of continually changing environments, UDA tends to underperform on previously seen domains when adapting to new ones - a problem known as catastrophic forgetting. To address this limitation, we introduce the EverAdapt framework, specifically designed for continuous model adaptation in dynamic environments. Central to EverAdapt is a novel Continual Batch Normalization (CBN), which leverages source domain statistics as a reference point to standardize feature representations across domains. EverAdapt not only retains statistical information from previous domains but also adapts effectively to new scenarios. Complementing CBN, we design a class-conditional domain alignment module for effective integration of target domains, and a Sample-efficient Replay strategy to reinforce memory retention. Experiments on real-world datasets demonstrate EverAdapt superiority in maintaining robust fault diagnosis in dynamic environments. Our code is available: https://github.com/mohamedr002/EverAdapt

翻译：无监督域自适应已成为数据驱动故障诊断中的关键解决方案，旨在解决模型在变化环境下性能下降的域偏移问题。然而，在持续变化的环境场景中，UDA在适应新域时往往在已见域上表现不佳——这一问题被称为灾难性遗忘。为突破此局限，我们提出EverAdapt框架，专门针对动态环境中的持续模型自适应而设计。其核心是新颖的持续批量归一化方法，该方法利用源域统计量作为参考基准，实现跨域特征表示的标准化。EverAdapt不仅能够保留历史域的统计信息，还能有效适应新场景。配合CBN模块，我们设计了类条件域对齐模块以实现目标域的有效融合，并采用样本高效回放策略以增强记忆保持能力。在真实数据集上的实验表明，EverAdapt在动态环境中保持鲁棒故障诊断能力方面具有显著优势。代码已开源：https://github.com/mohamedr002/EverAdapt