滚动轴承故障诊断研究综述
DOI:
https://doi.org/10.52810/FAAI.2024.001Keywords:
滚动轴承, 故障诊断, 深度学习, 迁移学习Abstract
滚动轴承作为旋转机械的核心部件，保持对轴承健康状态的监测能保证整个机械设备的正常运转。对于轴承故障检测方法的研究已经有了长久的发展，本文从传统故障检测方法到引入深度学习算法进行故障检测最后提出迁移学习在轴承故障诊断领域的应用进行了综述。传统的故障检测方法可以根据诊断步骤分为特征提取、故障识别两类，阐述了不同方法的适用条件以及应用缺陷。基于深度学习的轴承故障检测，尽管有了一定的发展，但是因其在数据和标签上的局限性，该类型的模型仍然有很大的发展空间和研究潜力。随着迁移学习方法的引入解决了在数据和标签上的限制，为轴承故障检测提供了新的思路和方向，并阐述了迁移学习方法目前面临的困境。
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