非线性系统故障诊断的粒子滤波方法
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摘要
针对粒子滤波存在粒子退化问题,提出一种基于无迹卡尔曼滤波和部分重采样的改进的粒子滤波算法.通过无迹卡尔曼滤波产生重要性分布函数和使用部分重采样算法进行重采样,以丰富粒子的多样性.并针对非线性系统故障诊断中非高斯背景下,似然函数检测量难以导出的问题,提出一种基于多模型和似然函数值的诊断方法.仿真结果表明:改进的滤波算法的估计精度优于标准的粒子滤波算法及其现有的两种改进算法,提出的故障诊断方法能够做到快速检测与准确隔离.
In order to solve particle degeneracy problem,w e present an improved particle filter algorithm based on unscented Kalman filter and partial resampling algorithm. By using unscented Kalman filter to generate importance distribution function and partial resampling algorithm to resample particles,the method enriches the diversity of the particles. Furthermore,to solve the problem w hich likelihood detection statistics is obtained w ith difficulty in typically nonlinear and nonGaussian,a fault diagnosis method based on the multiple model and the likelihood is proposed. Simulation results show the precision of the presented filter algorithm outperforms that of the standard particle filter and the improved particle filter existed in the filter system,and the proposed fault diagnosis method can detect fault quickly and isolate accurately.
In order to solve particle degeneracy problem,w e present an improved particle filter algorithm based on unscented Kalman filter and partial resampling algorithm. By using unscented Kalman filter to generate importance distribution function and partial resampling algorithm to resample particles,the method enriches the diversity of the particles. Furthermore,to solve the problem w hich likelihood detection statistics is obtained w ith difficulty in typically nonlinear and nonGaussian,a fault diagnosis method based on the multiple model and the likelihood is proposed. Simulation results show the precision of the presented filter algorithm outperforms that of the standard particle filter and the improved particle filter existed in the filter system,and the proposed fault diagnosis method can detect fault quickly and isolate accurately.
引文
[1]Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3):401-422.
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[3]Gordon N,Salmond D,Smith A.Novel approach to nonlinear and non-Gaussian Bayesian state estimation[J].IEE Proceedings on Radar,Sonar and Navigation,1993,140(2):107-113.
[4]刘先省,胡振涛,等.基于粒子优化的多模型粒子滤波算法[J].电子学报,2010,38(2):301-306.LIU Xian-xing,HU Zhen-tao,et al.A novel multiple model particle filter algorithm based on particle optimization[J].Acta Electronica Sinica,2010,38(2):301-306.(in Chinese)
[5]Van der Merwe.R,Doucet A,et al.The Unscented Particle Filter[EB/OL].http://citeseer.ist.psu.edu/325754.html,2006-11-16.
[6]王首勇,于兴伟.一种基于粒子滤波的雷达目标似然比检测方法[J].电子学报,2010,38(3):503-506.WANG Shou-yong,YU Xing-w ei.A likelihood ratio detection method of radar target based on particle filtering[J].Acta Electronica Sinica,2010,38(3):503-506.(in Chinese)
[7]Kadirkamanathan V,Li P,Jaward M H,et al.A sequential M onte Carlo filtering approach to fault detection and isolation in nonlinear systems[A].Proceedings of the 39th IEEE Conference on Decision and Control 2000[C].Sydney,Australla:IEEE,2000.4341-4346.
[8]Kadirkamanathan V,Li P,Jaward M H,et al.Particle filtering-based fault detection in nonlinear stochastic systems[J].International Journal of Systems Science,2002,33(4):259-265.
[9]F Alrowaie,R B Gopaluni,K E Kwok.Fault detection and isolation in stochastic non-linear state-space models using particle filters[J].Control Engineering Practice,2012,20(10):1016-1032.
[10]Bolic M,Djuric P M,Sangjin Hong.New resampling algorithms for particle filters[A]:Proc of International Conference on Acoustics,Speech,and Signal Processing(ICASSP)[C].Hong Kong:IEEE Press,2003.589-592.
[2]Crisan D,O Obanubi.Particle filters with random resampling times[J].Stochastic Processes and Their Application,2012,122(4):1332-1368.
[3]Gordon N,Salmond D,Smith A.Novel approach to nonlinear and non-Gaussian Bayesian state estimation[J].IEE Proceedings on Radar,Sonar and Navigation,1993,140(2):107-113.
[4]刘先省,胡振涛,等.基于粒子优化的多模型粒子滤波算法[J].电子学报,2010,38(2):301-306.LIU Xian-xing,HU Zhen-tao,et al.A novel multiple model particle filter algorithm based on particle optimization[J].Acta Electronica Sinica,2010,38(2):301-306.(in Chinese)
[5]Van der Merwe.R,Doucet A,et al.The Unscented Particle Filter[EB/OL].http://citeseer.ist.psu.edu/325754.html,2006-11-16.
[6]王首勇,于兴伟.一种基于粒子滤波的雷达目标似然比检测方法[J].电子学报,2010,38(3):503-506.WANG Shou-yong,YU Xing-w ei.A likelihood ratio detection method of radar target based on particle filtering[J].Acta Electronica Sinica,2010,38(3):503-506.(in Chinese)
[7]Kadirkamanathan V,Li P,Jaward M H,et al.A sequential M onte Carlo filtering approach to fault detection and isolation in nonlinear systems[A].Proceedings of the 39th IEEE Conference on Decision and Control 2000[C].Sydney,Australla:IEEE,2000.4341-4346.
[8]Kadirkamanathan V,Li P,Jaward M H,et al.Particle filtering-based fault detection in nonlinear stochastic systems[J].International Journal of Systems Science,2002,33(4):259-265.
[9]F Alrowaie,R B Gopaluni,K E Kwok.Fault detection and isolation in stochastic non-linear state-space models using particle filters[J].Control Engineering Practice,2012,20(10):1016-1032.
[10]Bolic M,Djuric P M,Sangjin Hong.New resampling algorithms for particle filters[A]:Proc of International Conference on Acoustics,Speech,and Signal Processing(ICASSP)[C].Hong Kong:IEEE Press,2003.589-592.