摘要
复杂设备的故障特征具有不确定性,非线性等特点.针对故障预测具有不确定性,将模糊数学中的直觉模糊集和灰色模型相结合设计故障预测的方法.新方法利用隶属度函数设计了描述系统运行正常的正常直觉模糊子集和运行异常的异常直觉模糊子集,利用灰色模型计算系统运行的预测值,并计算预测值的正常隶属度;再分别计算预测值的正常隶属度与正常直觉模糊子集和异常直觉模糊子集的贴近程度来实现故障预报.该方法通过三容水箱系统T2水箱水位变化预测三容水箱系统是否出现故障和通过UH-60行星齿轮盘裂纹何时开始增大的故障进行实验.实验验证了该方法的可行性,可及时准确地预测出系统故障.
The fault characteristics of complex equipment are characterized by uncertainty, nonlinearity and so on. For the uncertain fault prediction, we design a method of fault prediction,which combines intuitionistic fuzzy sets with grey model to predict fault. The new method uses the membership function to describe the normal system with the normal intuitionistic fuzzy sets and the abnormal system with the abnormal intuitionistic fuzzy sets, uses grey model to calculate predictive value, and uses membership function to calculate the membership degree. Then the fault prediction is implemented by calculating the closeness degree of predicted value of the normal membership degree with normal and abnormal intuitionistic fuzzy subset. This method predicts the fault of the three-tank-system when T2 tank starts to increase or decrease and the crack of the UH-60 planet gear plate when it starts to increase. The feasibility of the proposed method is verified by experiments, which can predict the failure of the system in time.
出处
《计算机系统应用》
2017年第4期29-34,共6页
Computer Systems & Applications
基金
国家自然科学基金(61572010)
福建省自然科学基金(2013J01223)
关键词
隶属度
贴近度
直觉模糊子集
灰色模型
故障预测
membership degree
closeness degree
intuitionistic fuzzy sets
grey model
fault prediction