引用本文:王金鑫,王忠巍,马修真,等.基于贝叶斯网络的柴油机润滑系统多故障诊断[J].控制与决策,2019,34(6):1187-1194
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基于贝叶斯网络的柴油机润滑系统多故障诊断
王金鑫,王忠巍,马修真,刘龙,袁志国
(哈尔滨工程大学动力与能源工程学院,哈尔滨150001)
摘要:
为解决柴油机润滑系统多故障的解耦与诊断问题,提出一种基于贝叶斯网络模型的故障诊断方法.建立的润滑系统贝叶斯网络诊断模型包括利用有向无环图描述多故障耦合关系和采用概率形式表示故障诊断定量知识两个部分.按照故障类型将润滑系统故障诊断任务分解为各类故障的诊断子任务,对于各子任务,利用故障树模型分析故障与征兆及多故障间的耦合关系,并通过故障树向贝叶斯网络的转化建立润滑系统的贝叶斯网络模型结构.在定量参数方面,采用noisy-OR/AND模型分析故障与征兆间的因果关联强度,通过设定故障的先验发生概率描述润滑系统的历史运行状况.最后,通过两起“进机油压过低”故障实例验证所提出方法的有效性.
关键词:  柴油机  润滑系统  多故障诊断  贝叶斯网络  故障树  noisy-OR/AND模型
DOI:10.13195/j.kzyjc.2017.1399
分类号:TK428
基金项目:国家自然科学基金项目(51305089);黑龙江省自然科学基金项目(E2016018).
Diagnosis of multiple faults of diesel engine lubrication system based on Bayesian networks
WANG Jin-xin,WANG Zhong-wei,MA Xiu-zhen,LIU Long,YUAN Zhi-guo
(College of Power and Energy Engineering,Harbin Engineering University,Harbin 150001,China)
Abstract:
Motivated by decoupling and diagnosing of the multiple faults of diesel engine lubrication system, a fault diagnosis method based on Bayesian networks is proposed. The establishment of a diagnosis Bayesian networks model for the lubrication system includes two processes: representing the coupling relationship between multiple faults through directed acyclic graph, and setting up the quantitative parameters in the form of probability. Before the implementation of the proposed method, the diagnostic task of the lubrication system is decomposed according to the types of failure. A fault tree model is utilized in every single diagnostic subtask to analyze the coupling between failures and symptoms and between different failures. The fault tree can be naturally converted into the topology of the lubrication system diagnosis Bayesian network. In the aspect of quantitative parameters, the noisy-OR/AND model is adopted to analyze the strength of causal relation between failures and symptoms. The historical health information can be represented through the prior probability of faults. The methodology proposed is illustrated by taking the failure‘low oil pressure’as an example. Testing results from two actual fault cases verify the effectiveness of the proposed methodology.
Key words:  diesel engine  lubrication system  multiple faults diagnosis  Bayesian networks  fault tree  noisy- OR/AND model

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