中国电机工程学报Issue(17)：2843-2850,8.DOI:10.13334/j.0258-8013.pcsee.2014.17.013
基于多分类相关向量机的水电机组振动故障诊断
Vibration Fault Diagnosis for Hydroelectric Generating Units Using the Multi-class Relevance Vector Machine
摘要
Abstract
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.关键词
相关向量机/水电机组/振动/故障诊断/多分类/决策导向图Key words
relevance vector machine/hydroelectric generating unit/vibration/fault diagnosis/multi-class/decision directed acyclic graph分类
信息技术与安全科学引用本文复制引用
易辉,梅磊,李丽娟,刘宇芳,袁宇浩..基于多分类相关向量机的水电机组振动故障诊断[J].中国电机工程学报,2014,(17):2843-2850,8.基金项目
国家自然科学基金项目(51205185，61273171)；2012年度江苏省“青蓝工程”中青年学术带头人项目；湖南省高校重点实验室开放基金(2013NGQ004)；江苏省高校自然科学基金项目(13KJB510013)。Project Supported by National Natural Science Foundation of China (51205185,61273171) （51205185，61273171）
2012 Qing Lan Project of Jiangsu Province；The Open Project of High School Key Laboratory of Hunan Province (2013NGQ004) （2013NGQ004）
Natural Science Foundation of Jiangsu High Schools (13KJB510013) （13KJB510013）
