EverAdapt：面向动态机器故障诊断环境的持续适应
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
AI总结：
针对动态故障诊断环境中无监督领域自适应的灾难性遗忘问题，提出EverAdapt框架，通过持续批归一化、类别条件领域对齐和样本高效回放策略实现持续模型适应，在真实数据集上验证了其优越性。
摘要：
无监督领域自适应（UDA）已成为数据驱动故障诊断中的关键解决方案，用于解决模型在变化环境中表现不佳的领域偏移问题。然而，在持续变化的环境下，UDA在适应新领域时往往在先前见过的领域上表现不佳——这一问题被称为灾难性遗忘。为了解决这一局限性，我们引入了EverAdapt框架，专门设计用于动态环境中的持续模型适应。EverAdapt的核心是一种新颖的持续批归一化（CBN），它利用源领域统计信息作为参考点，以标准化跨领域的特征表示。EverAdapt不仅保留了来自先前领域的统计信息，还能有效适应新场景。作为CBN的补充，我们设计了一个类别条件领域对齐模块用于目标领域的有效整合，以及一种样本高效的回放策略来强化记忆保留。在真实世界数据集上的实验证明了EverAdapt在动态环境中维持稳健故障诊断的优越性。我们的代码可用：https://github.com/mohamedr002/EverAdapt
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
