基于模糊贴近度的粒子滤波故障预测

复杂设备的故障特征具有不确定性，非线性等特点，为预防故障可能造成的严重后果，提高故障预测准确性是非常必要的.针对故障预测具有不确定性的特点，本文将模糊数学中的模糊贴近度和粒子滤波算法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模糊子集和运行异常的异常模糊子集，利用粒子滤波算法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模糊子集和异常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，并同基于改进余弦相似度的粒子滤波故障预报、基于随机摄动粒子滤波器的故障预报算法和基于粒子滤波的FDI方法进行了对比.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.

Particle Filter Fault Prediction Based on Fuzzy Closeness Degree

The fault characteristics of complex equipment are characterized by uncertainty, nonlinearity and so on. For prevention of failure may cause serious consequences, it is necessary to improve the accuracy of fault prediction. As fault prediction has the characteristics of uncertainty, we design a method of fault prediction, which combines fuzzy mathematics closeness degree with particle filter algorithm to predict fault. The new method uses the membership function to describe the normal system with the normal fuzzy sets and the abnormal system with the abnormal fuzzy sets, and uses particle filter algorithm to calculate predictive value and the membership degree. Then we can calculate the closeness degree of predicted value of the normal membership degree with normal and abnormal fuzzy subset to implement a fault prediction. This method predicts whether the three tank system is faulty by the change of water level of the T2 tank in the three tank system and makes test by the fault of the UH-60 planet gear disc when the crack begins to increase, and we have compared with Particle filter fault prediction based on Dynamic Time Warping match, Fault prediction algorithm based on stochastic perturbation particle filter and FDI method based on particle filter. The feasibility of the proposed method is verified by experiments, which can predict the failure of the system in time.