Deep Learning Algorithms for Bearing Fault Diagnostics - A Review

S. Zhang, Shibo Zhang, B. Wang, T. Habetler
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引用次数: 44

Abstract

This paper presents a comprehensive review on applying various deep learning algorithms to bearing fault diagnostics. Over the last ten years, the emergence and revolution of deep learning (DL) methods have sparked great interests in both industry and academia. Some of the most noticeable advantages of DL based models over conventional physics based models or heuristic based methods are the automatic fault feature extraction and the improved classifier performance. In addition, a thorough and intuitive comparison study is presented summarizing the specific DL algorithm structure and its corresponding classifier accuracy for a number of papers utilizing the same Case Western Reserve University (CWRU) bearing data set. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on healthy monitoring are also presented.
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轴承故障诊断的深度学习算法综述
本文综述了各种深度学习算法在轴承故障诊断中的应用。在过去的十年中,深度学习(DL)方法的出现和革命引起了工业界和学术界的极大兴趣。与传统的基于物理模型或启发式方法相比,基于深度学习的模型最显著的优点是自动故障特征提取和改进的分类器性能。此外,对使用相同凯斯西储大学(CWRU)轴承数据集的多篇论文进行了深入直观的比较研究,总结了具体的深度学习算法结构及其相应的分类器精度。最后,为了促进将各种DL算法应用于轴承故障诊断的过渡,针对具体的应用条件(如设置环境、数据大小、传感器数量和传感器类型)提供了详细的建议和建议。提出了进一步提高深度学习算法在健康监测方面性能的研究方向。
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