基于云加端的电机轴承故障诊断应用研究

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Abstract Aiming at the problems of poor real-time capability, limited learning capability and difficult implementation of the existing fault diagnosis system, an improved Cloud and Terminal Support Vector Machine (CaTSVM) is proposed in this paper and applied to motor Bearing fault diagnosis. The CaTSVM method runs the traditional fault diagnosis feature extraction and feature classification in terminal devices and cloud devices respectively and introduces "Pipeline" data processing structure into the CaTSVM method, which effectively improves the real-time performance of the method. In the cloud, the Cloud Feature Mode Library (CFML) is built, and fault features are selectively added to the model library. In the traditional offline SVM training, online SVM training is used, and the updated fault features are selectively used to train the SVM model , To further improve its classification capabilities, so that diagnostic systems have the "lifelong learning" ability. After a large number of experimental verification, the use of cloud plus end method significantly improves the diagnostic accuracy, and promote the practical application of fault diagnosis.

Key words： Cloud and terminal SVM On-line training Feature mode library Fault diagnosis Pipeline

Received: 14 December 2017 Published: 28 April 2019

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	Articles by authors
	GENG Xiaoqiang1
	TANG Xianghong1
	2
	3
	LU Jianguang1
	2
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Cite this article:

GENG Xiaoqiang1,TANG Xianghong1,2, et al. Application of motor bearing fault diagnosis based on cloud and terminal[J]. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(9): 223-230.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2019/V38/I9/223

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