基于多源信息融合的柴油机故障诊断方法

Diesel Engine Fault Diagnosis Method Based on Multi Source Information Fusion

为提高柴油机这一复杂系统的故障诊断精度,在采集缸盖声发射信号、缸盖振动信号和机体声发射信号特征信息的基础上,利用3个BP神经网络进行局部诊断并获得证据体,对各证据体应用D S证据组合规则进行决策融合。针对D S证据理论在处理冲突证据时存在的局限性问题,提出一种多源加权融合的故障诊断方法。该方法利用各证据体对命题识别的正确率和证据体之间的距离对各证据体进行修正。故障模拟试验表明:提出的方法可提高柴油机的故障诊断精度,充分验证其应用在柴油机故障诊断中的可行性。

In order to improve the fault diagnosis accuracy of the complex system of diesel engine,based on the acquisition of the characteristic information of the sound emission signal of the cylinder head,the vibration signal of the cylinder head and the acoustic emission signal of the cylinder block,three BP neural networks are used for local diagnosis and obtaining the evidence bodies and D S evidence combination rules are applied to each evidence body for decision fusion.In view of the limitations of D S evidence theory in dealing with conflicting evidence,a fault diagnosis method based on multi source weighted fusion is proposed.The correct rate of recognizing all the propositions and the distance between evidence bodies are used to correct each body of evidence.The fault simulation experiment shows that the proposed method can improve the accuracy of diesel engine fault diagnosis and fully verify the feasibility of its application in diesel engine fault diagnosis.