基于模糊贴近度的粒子滤波故障预测
|
摘要
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.
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.
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.
引文
1王爱侠,张立颖.基于选择判据与贴近度的电网故障诊断算法.控制与决策,2016,(1):155–159.
2 孙致远,郑坚,熊超,等.基于FMEA和模糊贴近度的装备故障维修方法选择.火炮发射与控制学报,2015,36(3):86–90.
3 Du JY.The prediction of grounding grid corrosion rate based on vector similarity and fuzzy closeness.Journal of Information&Computational Science,2015,12(3):1169–1181.
4 梁国坚,梁冠安.用模糊集贴近度法识别变压器的故障电流和励磁涌流.变压器,1998,(1):32–37.
5 黄小龙,刘维亭.基于模糊贴近度的故障诊断.科学技术与工程,2012,12(30):8111–8115.
6 陈举华,郭毅之.GM模糊优化方法在小子样机械系统故障预测中的应用.中国机械工程,2002,13(19):1658–1660.
7 姚良,成曙,张振仁.基于小波包频带能量分解和欧氏贴近度的柴油机气阀机构故障诊断.机电工程技术,2006,35(1):32 –35.
8 耿俊豹,黄树红,陈非,等.基于信息熵贴近度的旋转机械故障诊断.华中科技大学学报:自然科学版,2006,34(11):93–95.
9 陈非,黄来,韩彦广,等.基于信息空间贴近度的旋转机械故障诊断方法.汽轮机技术,2013,55(4):297–299.
10 胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005,20 (4):361–365.
11 张琪,胡昌华,乔玉坤,等.基于随机摄动粒子滤波器的故障预报算法.控制与决策,2009,24(2):284–288.
12 Arulampalam MS,Maskell S,Gordon NJ,Clapp T.A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking.IEEE Trans.on Signal Processing,2002,50 (2):174–188.
13 蒋欣,王开军,陈黎飞.基于改进余弦相似度的粒子滤波故障预报.计算机系统应用,2015,24(1):98–103.
14 Zadeh LA.Fuzzy sets.Information&Control,1965,8(65):338 –353.
15 刘法贵,赵娟.模糊贴近度及应用.华北水利水电学院学报,2006,27(3):104–106.
16 刘凯,梁晓庚,李友年.基于粒子滤波的非线性目标跟踪算法研究.四川兵工学报,2014,(11):14–17.
17 陈永义,吴望名,黄金丽,陈图云,等.应用模糊集方法.北京:北京师范大学出版社,1985:52–58.
18 Orchard ME,Vachtsevanos GJ.A particle-filtering approach for on-line fault diagnosis and failure prognosis.Trans.of the Institute of Measurement&Control,2009,31(3-4):221 –246.
19 冯驰,吕晓凤,汲清波.粒子滤波理论及其在目标跟踪中的应用.计算机工程与应用,2008,44(6):246–248.
2 孙致远,郑坚,熊超,等.基于FMEA和模糊贴近度的装备故障维修方法选择.火炮发射与控制学报,2015,36(3):86–90.
3 Du JY.The prediction of grounding grid corrosion rate based on vector similarity and fuzzy closeness.Journal of Information&Computational Science,2015,12(3):1169–1181.
4 梁国坚,梁冠安.用模糊集贴近度法识别变压器的故障电流和励磁涌流.变压器,1998,(1):32–37.
5 黄小龙,刘维亭.基于模糊贴近度的故障诊断.科学技术与工程,2012,12(30):8111–8115.
6 陈举华,郭毅之.GM模糊优化方法在小子样机械系统故障预测中的应用.中国机械工程,2002,13(19):1658–1660.
7 姚良,成曙,张振仁.基于小波包频带能量分解和欧氏贴近度的柴油机气阀机构故障诊断.机电工程技术,2006,35(1):32 –35.
8 耿俊豹,黄树红,陈非,等.基于信息熵贴近度的旋转机械故障诊断.华中科技大学学报:自然科学版,2006,34(11):93–95.
9 陈非,黄来,韩彦广,等.基于信息空间贴近度的旋转机械故障诊断方法.汽轮机技术,2013,55(4):297–299.
10 胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005,20 (4):361–365.
11 张琪,胡昌华,乔玉坤,等.基于随机摄动粒子滤波器的故障预报算法.控制与决策,2009,24(2):284–288.
12 Arulampalam MS,Maskell S,Gordon NJ,Clapp T.A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking.IEEE Trans.on Signal Processing,2002,50 (2):174–188.
13 蒋欣,王开军,陈黎飞.基于改进余弦相似度的粒子滤波故障预报.计算机系统应用,2015,24(1):98–103.
14 Zadeh LA.Fuzzy sets.Information&Control,1965,8(65):338 –353.
15 刘法贵,赵娟.模糊贴近度及应用.华北水利水电学院学报,2006,27(3):104–106.
16 刘凯,梁晓庚,李友年.基于粒子滤波的非线性目标跟踪算法研究.四川兵工学报,2014,(11):14–17.
17 陈永义,吴望名,黄金丽,陈图云,等.应用模糊集方法.北京:北京师范大学出版社,1985:52–58.
18 Orchard ME,Vachtsevanos GJ.A particle-filtering approach for on-line fault diagnosis and failure prognosis.Trans.of the Institute of Measurement&Control,2009,31(3-4):221 –246.
19 冯驰,吕晓凤,汲清波.粒子滤波理论及其在目标跟踪中的应用.计算机工程与应用,2008,44(6):246–248.