With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

中文翻译：

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基于深度学习的旋转机械故障诊断应用

随着现代工业的不断进步，旋转机械正逐步向复杂化、智能化方向发展。旋转机械故障诊断技术是保证设备正常运行和安全生产的关键手段之一，具有十分重要的意义。深度学习是分析和处理大数据的有用工具，已广泛应用于各个领域。在简要回顾了早期故障诊断方法之后，本文重点介绍深度学习中广泛使用的方法模型：深度置信网络（DBN）、自编码器（AE）、卷积神经网络（CNN）、循环神经网络（RNN） ，生成对抗网络（GAN），从原理和在旋转机械故障诊断领域的应用两个方面总结了迁移学习方法。然后，总结了常用的评价旋转机械故障诊断方法性能的评价指标。最后，根据目前旋转机械故障诊断领域的研究现状，讨论了当前存在的问题以及未来可能的发展和研究趋势。