考虑跨域环境与工况变化的旋转机械故障诊断持续学习
Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations
AI总结：
针对旋转机械故障诊断中现实工况与环境变异性导致模型难以可靠检测的问题，本研究提出持续深度学习方法，解决灾难性遗忘等二阶挑战，实验获88.96%平均域精度及低遗忘度量，提升跨域适应能力。
摘要：
尽管存在大量机器学习模型可检测滚动轴承应变和变形等问题（通常由安装不当、过载或润滑不良引起），但这些模型往往难以从现实工况和环境变异性的噪声中分离故障。可变负载、高温、应力和转速等条件会掩盖早期故障迹象，使可靠检测变得具有挑战性。为解决这些局限，本研究提出一种持续深度学习方法，能够跨随时间共享底层结构的域进行学习。该方法超越传统精度指标，解决四个二阶挑战：灾难性遗忘（新学习覆盖旧知识）、缺乏可塑性（模型无法适应新数据）、前向迁移（利用旧知识改进未来学习）和后向迁移（用新域见解完善旧知识）。该方法包含特征生成器和域特定分类器，能在新域出现时以最小干扰增长容量，同时经验重放机制选择性回顾先前域以减轻遗忘。此外，通过优先重放具有最高先前误差的域来利用跨域非线性依赖，基于最具信息量的过往经验优化模型。实验显示，在非平稳类增量环境中，平均域精度高达88.96%，遗忘度量低至0.0027。
英文摘要：
Although numerous machine learning models exist to detect issues like rolling bearing strain and deformation, typically caused by improper mounting, overloading, or poor lubrication, these models often struggle to isolate faults from the noise of real-world operational and environmental variability. Conditions such as variable loads, high temperatures, stress, and rotational speeds can mask early signs of failure, making reliable detection challenging. To address these limitations, this work proposes a continual deep learning approach capable of learning across domains that share underlying structure over time. This approach goes beyond traditional accuracy metrics by addressing four second-order challenges: catastrophic forgetting (where new learning overwrites past knowledge), lack of plasticity (where models fail to adapt to new data), forward transfer (using past knowledge to improve future learning), and backward transfer (refining past knowledge with insights from new domains). The method comprises a feature generator and domain-specific classifiers, allowing capacity to grow as new domains emerge with minimal interference, while an experience replay mechanism selectively revisits prior domains to mitigate forgetting. Moreover, nonlinear dependencies across domains are exploited by prioritizing replay from those with the highest prior errors, refining models based on most informative past experiences. Experiments show high average domain accuracy (up to 88.96%), with forgetting measures as low as .0027 across non-stationary class-incremental environments.
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。
