【作者】 易辉； 丁达宾； 卢铭； 李丽娟；

【Author】 Graph Hui Yi1, Dabin Ding2, Ming Lu3, Lijuang Li1 1.College of Automation and Electronic Engineering, Nanjing University of Technology, Nanjing 2118162.Shanghai Institute of Spaceflight Control Technology, Shanghai, 2002333.CSIS No.724 Institute, Nanjing, 210003

【机构】 南京工业大学自动化与电气工程学院； 上海航天控制技术研究所； 中船重工第七二四研究所；

【摘要】 相关向量机(Relevance vector machine,RVM)是一种基于贝叶斯统计学习理论的新型二分类器.与传统支持向量机(Support Vector Machine,SVM)方法相比,该方法在学习过程中避免了复杂的参数设置,同时在输出结果时更给出了分类的可靠性,更适合实际工程应用.本文采用决策导向无环图(Decision Directed Acyclic Graph,DDAG)方法将RVM进行多分类扩展,使其能够对多类故障模式进行识别,进而实现故障定位.相比传统方法,基于RVM和DDAG的诊断方法具有更小的计算复杂度和更高的故障诊断可靠性.所提方法被用于牵引电机故障定位实验,效果良好,验证了算法的有效性.更多还原

【Abstract】 Relevance Vector Machine (RVM) is one of the ’state-of-the-art’ approaches for classification which exploits the probabilistic Bayesian learning frame work. Compared with the classical Support Vector Machine(SVM), RVM avoids the problem of parameter setting while learning and offers probabilistic outputs. These make RVM more suitable for real applications. In this paper, we have employed the DDAG approach to extend the RVM into a multi-classifier which enables its recognition of different faulty patterns, and further makes the fault location feasible. Compared with conventional methods,The proposed approach yields a smaller computing complexity whereas it maintains a higher diagnostic reliability. It has also been applied to the real problem of pulling motor fault isolation, and satisfactory results have been obtained in these experiments which has validated the effectiveness of proposed approach.更多还原