摘要
水电机组振动故障成因与故障征兆之间呈复杂的非线性关系,传统方法难以描述。当前研究常采用模式识别方法,如支持向量机、神经网络等实现振动故障诊断。该文在现有研究基础上,引进相关向量机(relevance vector machine,RVM)对诊断过程进行改进。相比传统方法,该文所提方法在学习过程中参数设置简单,在输出结果时给出了分类的可靠性,适合实际工程应用。同时,该方法在决策过程中,能够根据训练数据分布情况,自动选取决策结构,进一步提高诊断的速度与准确性。将该文所提诊断方法用于水电机组振动故障诊断实例,取得良好效果,验证了算法的有效性。
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.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2014年第17期2843-2850,共8页
Proceedings of the CSEE
基金
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)~~
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
relevance vector machine
hydroelectricgenerating unit
vibration
fault diagnosis
multi-class
decision directed acyclic graph
