Vibration fault diagnosis for hydroelectric generating units using the multi-class relevance vector machine

被引：0

作者：

Yi, Hui ^{[1
]}

Mei, Lei ^{[1
]}

Li, Lijuan ^{[1
]}

Y., Liu

Y., Yuan

机构：

[1] College of Automation and Electronic Engineering, Nanjing University of Technology, Nanjing 211816, Jiangsu Province

[2] Guodian Institute of Environmental Protection, Nanjing 210031, Jiangsu Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2014年 / 34卷 / 17期

关键词：

Decision directed acyclic graph; Fault diagnosis; Hydroelectric generating unit; Multi-class; Relevance vector machine; Vibration;

D O I：

10.13334/j.0258-8013.pcsee.2014.17.013

中图分类号：

学科分类号：

摘要：

The functions between vibrating fault symptoms and their causes for hydroelectric generating units are nonlinear, and are hard to be described by conventional approaches. One usual method for the vibrating fault diagnosis is to use the pattern recognition approaches like the support vector machine and neural networks. Following the current work, we proposed the Relevance Vector Machine (RVM) based approach to optimize the diagnostic performance. Compared with conventional approaches, the proposed approach avoids the problem of parameter setting while learning, and offers probabilistic outputs. These make RVM more suitable for real applications; Moreover, the proposed approach could automatically select the optimal decision structure according to the training sample distribution, and increase the diagnostic speed and accuracy. Finally, we applied the proposed approach to a real diagnosis of the Hydroelectric Generating Unit vibrating faults, and satisfactory results have been obtained in the experiments which have validated the effectiveness of the proposed approach. © 2014 Chinese Society for Electrical Engineering.

引用

页码：2843 / 2850

页数：7

共 31 条

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