引用本文:王金鑫,王忠巍,马修真,等.柴油机燃油系统多故障的解耦与诊断技术[J].控制与决策,2019,34(10):2249-2255
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柴油机燃油系统多故障的解耦与诊断技术
王金鑫,王忠巍,马修真,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:
柴油机燃油系统多故障的强关联耦合给其诊断过程带来严重的不确定性,同时导致建立诊断模型也往往依赖大量的先验知识,多故障的解耦与诊断已成为柴油机燃油系统故障诊断研究中的一大技术难题.针对该问题,提出一种基于简化模型结构和定量参数的贝叶斯网络诊断方法.在模型结构方面,利用粗糙集理论中的属性约简方法评估故障信息的等价关系,去除冗余故障特征,简化贝叶斯网络诊断模型的拓扑结构;在定量参数方面,采用因果机制独立模型分析故障事件的因果关联强度,将多故障对同种征兆的耦合影响解耦为单故障下的因果机制,模型所需的条件概率数量减化为故障数的线性形式.应用该诊断方法,燃油系统贝叶斯网络诊断模型所需的先验知识大幅减少,显著降低了该模型建立和应用的复杂程度.
关键词:  柴油机  燃油系统  多故障  故障解耦  贝叶斯网络  简化模型
DOI:10.13195/j.kzyjc.2018.0264
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).
Decoupling and diagnosis of multi-fault of diesel engine fuel system
WANG Jin-xin,WANG Zhong-wei,MA Xiu-zhen,YUAN Zhi-guo
(College of Power and Energy Engineering,Harbin Engineering University,Harbin150001,China)
Abstract:
The strong correlation and coupling of multi-fault of diesel engine fuel systems brings considerable amount of uncertainty to its diagnosis process. Meanwhile, establishing the diagnosis model always calls for massive prior knowledge because of the foregoing reason. The decoupling and diagnosis of the multi-fault has developed into a great technical difficulty in the study on the failure diagnosis of the diesel fuel system. Regarding this problem, a diagnosis method is proposed based on Bayesian networks with simplified model structure and quantitative parameters. In the aspect of model structure, the attribute reduction method in the rough sets theory is utilized to evaluate the equivalent relation between the information about failures. And on this basis, the redundant failure characteristics are removed and the topological structure of the Bayesian networks diagnosis model is simplified ultimately; in the aspect of quantitative parameters, the independent model of the casual mechanism is adopted to analyze the strength of causal relation for the failure events. The coupling effects of multi-fault to the same symptom are decoupled to the ones under a single failure and the quantity of the conditional probability needed by the model is simplified into the linear form of the failure quantity. By adopting the diagnosis method proposed, the prior knowledge needed by the Bayesian networks diagnosis model of the diesel engine fuel system is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model.
Key words:  diesel engine  fuel system  multi-fault  failure decoupling  Bayesian networks  simplified model

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