| 引用本文: | 丁石川,厉雪衣,杭俊,等.深度学习理论及其在电机故障诊断中的研究现状与展望[J].电力系统保护与控制,2020,48(8):172-187. |
| DING Shichuan,LI Xueyi,HANG Jun,et al.Deep learning theory and its application to fault diagnosis of an electric machine[J].Power System Protection and Control,2020,48(8):172-187 |
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| 深度学习理论及其在电机故障诊断中的研究现状与展望 |
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丁石川1,2,厉雪衣1,2,杭 俊1,2,王尹江1,2,王群京1,2
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(1.安徽大学电气工程与自动化学院, 安徽 合肥 230601; 2.安徽大学高节能电机及控制技术国家地方联合工程实验室，安徽 合肥 230601)
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| 摘要: |
| 电机已经被广泛应用到人们生产生活的各个领域中，电机的故障不但会对电机本身会造成损害，甚至会引发经济损失、人员伤亡等各种问题。因此，将及时且高效的故障诊断技术应用于电机有着重要意义。相比较传统故障诊断技术而言，深度学习因其更强大更复杂的数据表达能力，已被应用于电机故障诊断领域，并取得了一定的研究成果。因此，介绍了深度置信网络(DBN)、自编码网络(AE)、卷积神经网络(CNN)和循环神经网络(RNN)这四类经典的深度学习模型，并总结了这四类模型在电机故障诊断中的应用。最后对深度学习在电机故障诊断领域中所面临的问题和挑战进行了总结和展望。 |
| 关键词: 电机 故障诊断 深度学习 深度置信网络 自编码网络 卷积神经网络 循环神经网络 |
| DOI：10.19783/j.cnki.pspc.190712 |
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| 基金项目:国家自然科学基金项目资助(51637001, 51607001,51507002);安徽省自然科学基金项目资助(1508085ME87,1708085QE108) |
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| Deep learning theory and its application to fault diagnosis of an electric machine |
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DING Shichuan1,2,LI Xueyi1,2,HANG Jun1,2,WANG Yinjiang1,2,WANG Qunjing1,2
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(1. School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China;2. National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China)
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| Abstract: |
| The electric machine has been widely used in various fields, and its failure may not only cause damage to the machine, but also many problems, such as economic loss, casualties and so on. Therefore, it is important to apply timely and efficient fault diagnosis technology. Deep learning has been applied in fault diagnosis of electric machines and obtained some useful results because of its more powerful and more complex feature expression ability than traditional techniques. Hence, this paper introduces four classic types of deep learning model, the Deep Belief Networks (DBN), Auto-Encoders (AE), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and summarizes the application of these four deep learning models in electric machine fault diagnosis. Finally, the problems and challenges that deep learning faces in this application are summarized and prospects discussed. This work is supported by National Natural Science Foundation of China (No. 51637001, No. 51607001, and No. 51507002) and Natural Science Foundation of Anhui Province (No. 1508085ME87 and No. 1708085QE108). |
| Key words: electric machine fault diagnosis deep learning deep belief network auto-encoders convolutional neural networks recurrent neural network |
