噪声方差自适应修正的混合系统故障诊断方法

王强 1,2,刘永葆 1,2,贺星 1,2, 刘树勇1

振动与冲击 ›› 2016, Vol. 35 ›› Issue (8) : 14-20.

PDF(2442 KB)
PDF(2442 KB)
振动与冲击 ›› 2016, Vol. 35 ›› Issue (8) : 14-20.
论文

噪声方差自适应修正的混合系统故障诊断方法

  • 王强 1,2 , 刘永葆 1,2,贺星 1,2, 刘树勇1
作者信息 +

Hybrid System Fault Diagnosis Method Based on Noise Variance Adaptive Correction

  • Wang Qiang1,2  Liu Yong-bao1,2, He Xing1,2 ,Liu Shu-yong1
Author information +
文章历史 +

摘要

针对混合系统故障诊断问题,提出了一种模型噪声方差自适应修正的多模态故障诊断方法。首先,在粒子滤波的框架内将混合系统故障诊断建模为最优状态估计与跟踪问题,利用实时观察信息和各个模态先验的转移概率,估计最优的故障模态,并针对估计结果进行单独的建模分析;接着,根据平滑估计值和当前观测信息之间的相关性,建立噪声方差在线自适应检测机制,对模态噪声方差进行自适应更新,有效克服了模型噪声统计特性时变对滤波精度的影响,提升了算法的鲁棒性。最后,针对多种模态估计跟踪进行了充分的仿真分析,验证了本文方法的有效性和鲁棒性。

Abstract

 For hybrid system fault diagnosis problem in noise statistics properties time-varying, this paper proposed a multimodal fault diagnosis method based on noise variance adaptive relevant correction. First of all, within the framework of particle filter, hybrid system fault diagnosis is modeled as optimal state estimation and tracking problem, and real-time observation information and each modal prior transition probability is used to estimate the optimal fault mode. The estimating results are modeled separately for incoming analysis. Second, the noise variance adaptive online detection mechanism is built based on the correlation between smoothing estimation and the observation information, and updates the modal noise variance adaptively, which effectively overcomes the filter shift problem results from the time-varying noise statistical properties. The proposed method improves the robustness effectively. Finally, the experiments of three kinds failure modes show that the proposed method is efficient and robust.

关键词

混合系统 / 故障诊断 / 粒子滤波 / 噪声统计特性 / 自适应滤波

Key words

Hybrid system / Fault detection / Particle filter / Noise statistical properties / Adaptive filter

引用本文

导出引用
王强 1,2,刘永葆 1,2,贺星 1,2, 刘树勇1. 噪声方差自适应修正的混合系统故障诊断方法[J]. 振动与冲击, 2016, 35(8): 14-20
Wang Qiang1,2 Liu Yong-bao1,2, He Xing1,2,Liu Shu-yong1. Hybrid System Fault Diagnosis Method Based on Noise Variance Adaptive Correction[J]. Journal of Vibration and Shock, 2016, 35(8): 14-20

参考文献

[1] 莫以为,萧德云. 基于进化粒子滤波器的混合系统故障诊断[J]. 控制与决策,2004,19(6):611-615.
MO Yi-wei, XIAO De-yun. Fault diagnosis of  hybrid systems based on the evolutionary particle  filter[J]. Control and Decision,2004,19(6): 611-615.
[2] Wong M L D, Jack L B, Nandi A K. Modified self-organising map for automated novelty detection applied to vibration signal monitoring [J]. Mechanical Systems and Signal Processing, 2006, 20: 593-610.
[3] Lei Y G, He Z J, Zi Y Y. Application of an intelligent fault diagnosis  method to rotating machinery[J]. Expert Systems with Applications, 2009, 36: 9941-9948.
[4] 田承伟, 宗长富, 姜国彬,等. 基于双自适应Kalman滤波的线控转向汽车传感器故障诊断[J].中国公路学报,2009,22(4):115-121.TIANCheng-wei, ZONGChang-fu, JIANGGuo-bin, etal. Sensor Fault Diagnosis for Steer-by-Wire Car Based on Dual Adaptive Kalman Filter[J]. China Journal of High way and Transport, 2009,22(4):115-121.
[5] M.S. Arulampalam, S. Maskell,Neil Gordon, etal. A Tutorial on Particle Filters for On line Non-linear Non-Gaussian Bayesian Tracking[J].IEEE Transactions on Signal Processing, 2002,50(2):174-188.
[6] Bing-Fei Wu, Chih-Chung Kao, Cheng-Lung Jen, et al. A Relative Discriminative Histogram of Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking[J]. IEEE Transaction on industrial electronics, 2014, 61(8):4228-4237.
[7] Yuanqing Xia, Jingjing Yan, Peng Shi, etal. Stability Analysis of Discrete-Time Systems with Quantized Feedback and Measurements. IEEE Transactions on Industrial Informatics. 2013, 9(1): 313-324.
[8] Amrita Mishra, R. Gayathri, Aditya K. Jagannatham. Random Parameter EM-Based Kalman Filter(REKF) for Joint Symbol Detection and Channel Estimation in Fast Fading STTC MIMO Systems[J]. IEEE Signal Processing Letters, 2014, 21(6): 766-770.
[9] Jian-fang Dou, Jian-xun Li. Robust visual tracking base on adaptively multi-feature fusion and particle filter[J]. Optik, 2014,(125):1680-1686.
[10] Yang W X. Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming[J]. Journal of Sound and Vibration, 2006, 293:213-226.
[11] Lei Y G, He Z , Zi Y Y, et al. A new approach to intelligent fault diagnosis of rotating machinery[J]. Expert Systems with Applications, 2008, 35:1593-1600.
[12] V.  Kadirkamanathan, P. Li, M. H. Jaward and and etc. A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems[C]. Proceedings of IEEE Conference on Decision and Control, 2000,5: 4341-4346.
[13] Ping Li, Visakan Kadirkamanathan. Fault detection and isolation in Nonlinear stochastic systems-A combined adaptive Monte Carlo filtering and likelihood ratio approach[J]. International Journal of  Control, 2004,77( 12) : 1101-1114.
[14] Jardine A K S,Lin D,Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance[J]. Mechanical Systems and Signal Processing, 2006, 20::1483-1510.
[15] Lei Y G, He Z , Zi Y Y, et al. New clustering algorithm based fault diagnosis using compensation distance evaluation technique[J]. Mechanical Systems and Signal Processing, 2008, 22:419 -435.
[16] Katsuji U, Toshiharu H. Evolution strategies based particle filters for fault detection[C]. IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007,(1-5)::58-65.
[17] Bo Zhao, Roger Skjetne, Mogens Blanke, etal. Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot[J].IEEE Transactions on control systems technology, 2014,22(6):2399-2407
[18] MO Yi-wei, XIAO De-yun. Hybrid System Monitoring and Diagnosing Based on Particle Filter Algorithm[J]. Acta Automatica Sinica, 2003,29(5):641-648.
[19] Lei Y G, He Z, Zi Y Y. Application of a novel hybrid intelligent method to compound fault diagnosis of locomotive roller bearings[J]. ASME Transactions on Journal of Vibration and Acoustics, 2008,130: 1-6.
[20] 胡振涛,潘泉,杨峰等. 基于CRPF的残差似然比检验故障诊断算法[J]. 系统工程与电子技术,2009,31(12):3022-3025.
HU Zhen-tao, PAN Quan,YANG Feng, and etc..  Residual likelihood ratio test for fault diagnosis  based on cost reference particle filter[J]. Systems  Engineering and Electronics, 2009,31(12): 3022-3025.
[21] Ping Li, Visakan Kadirkamanatha. Particle Filtering Based Likelihood Ratio Approach to Fault Diagnosis in Nonlinear Stochastic Systems[J]. IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, 2001, 31(3):337-343.
[22] Siamak Tafazoli,  Xuehong Sun. Hybrid System State Tracking and Fault Detection Using Particle Filters[J].IEEE Transactions on control systems technology, 2006,14(6):1078-1087
 

PDF(2442 KB)

Accesses

Citation

Detail

段落导航
相关文章

/