摘要
现有测试性模型对复杂装备进行分层建模时,不仅需要每层装备的故障模式、影响和危害性分析(failure mode,effects and criticality analysis,简称FMECA),还需要确定各故障模式之间的联系,增加了实际工作量和建模难度,与实际故障诊断脱节。为解决上述问题,提出一种基于Petri网的建模方法,将测试性模型与故障诊断模型相结合。首先,采用广义随机Petri网建立装备系统级的测试性模型,采用模糊Petri网(fuzzy Petri net,简称FPN)建立子系统的故障诊断模型,完成系统到子系统的传递;其次,根据FMECA信息对故障统计数据进行处理,通过神经网络对参数进行调整学习和优化;然后,采用正向推理实现故障的准确预测,逆向推理结合最小割集完成故障定位;最后,以涡扇发动机风扇部件模型为例进行建模分析,并通过故障树和统计数据验证了模型的正确性和有效性。
Existing test models for hierarchical modeling of complex equipment require not only failure mode,effects and criticality analysis(FMECA)information for each layer of equipment,but also the relationship between failure modes,which increases the actual workload and modeling difficulty,and is disconnected from the actual fault diagnosis.In order to solve the above problems,a modeling method based on Petri nets is proposed,which combines testability models with fault diagnosis models.Stochastic Petri nets are used to establish test systems at the equipment system level,and fuzzy Petri nets(FPN)are used to establish subsystem fault diagnosis model,completing the transfer of system to subsystem.Fault statistics are processed according to FMECA information,the parameters are studied and optimized by neural network.The forward inference is used to predict the fault accurately,reverse inference and minimum cut set are used to locate the fault.Finally,the turbofan engine fan component model is taken as an example to perform inference analysis,and the correctness and rationality of the inference model is verified through fault trees and statistical data.
作者
翟禹尧
史贤俊
韩露
秦玉峰
ZHAI Yuyao;SHI Xianjun;HAN Lu;QIN Yufeng(Coast Guard Academy,Naval Aviation University Yantai,264001,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2022年第2期335-342,410,共9页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金青年科学基金资助项目(61903374)。
