贝叶斯网络和模糊评判结合的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearing Based on Bayesian Network and Fuzzy Evaluation

作　　者：马德仲[1] 任锁 刘凯辛 李明 周真[1] MA De-zhong;REN Suo;LIU Kai-xin;LI Ming;ZHOU Zhen(School of Measurement-control Technology and Communications Engineering,Harbin University of Science and Technology, The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province,Harbin 150080,China)

机构地区：[1]哈尔滨理工大学测控技术与通信工程学院,测控技术与仪器黑龙江省高校重点实验室,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2018年第5期113-118,共6页Journal of Harbin University of Science and Technology

基　　金：黑龙江省自然科学基金面上项目(F201305).

摘　　要：针对大型复杂系统在诊断的过程中,由于现有方法主要通过一系列方法来提高诊断的效果,而缺乏考虑诊断过程中的检测难度、检测速度和检测经济性等因素。提出了贝叶斯网络诊断与多因素模糊综合评判相结合进行故障诊断的方法,在诊断的过程中不仅考虑故障概率,而且结合检测方法难易程度、检测速度、检测的准确性和经济性等方面,得到诊断的优化方法。通过对齿轮箱滚动轴承故障进行诊断的实例,可以明显看出该方法在综合诊断过程中的优势。研究成果可以作为对大型复杂系统进行故障诊断的优化方法,从而科学指导维修方案。In the process of diagnosing large and complex system,the existing methods mainly improve the diagnosis effect by a series of methods,but lack the consideration of the difficulty of detection,the detection speed and the detection economy.In this paper,Bayesian network diagnosis and multi-factor fuzzy comprehensive evaluation are combined to diagnose the fault.In the process of diagnosis,not only the probability of failure is considered,but also the difficulty of detection,the speed of detection,the accuracy and economy of detection,and the optimization method is obtained.The advantages of this method in the comprehensive diagnosis process can be clearly seen by the example of diagnosing the fault of gearbox bearing.The research results can be used as an optimization method for fault diagnosis of large and complex systems,so as to guide the maintenance plan scientifically.

关 键 词：贝叶斯网络 模糊综合评判 诊断优化 

分 类 号：X928.7[环境科学与工程—安全科学]