基于异步IMM融合滤波的网络化系统故障诊断

胡艳艳; 金增旺; 薛晓玲; 孙长银

doi:10.16383/j.aas.2017.c160768

基于异步IMM融合滤波的网络化系统故障诊断

doi: 10.16383/j.aas.2017.c160768

1.
北京科技大学自动化学院北京市工业波谱成像工程技术研究中心北京 100083
2.
东南大学自动化学院南京 210096

基金项目:

国家自然科学基金 61520106009

国家自然科学基金 61533008

国家自然科学基金 61304105

详细信息

作者简介:
胡艳艳    北京科技大学自动化学院讲师.2011年获得清华大学自动化系博士学位.主要研究方向为故障诊断, 故障预测和信息融合.E-mail:huyanyan@ustb.edu.cn

金增旺    北京科技大学自动化学院博士研究生.2013年获得北京科技大学自动化学院学士学位.主要研究方向为估计融合, 故障诊断及预测、事件驱动系统.E-mail:b20130374@xs.ustb.edu.cn

薛晓玲    北京科技大学自动化学院硕士研究生.2015年获得天津师范大学学士学位.主要研究方向为信息融合, 故障诊断.E-mail:xuexiaoling@xs.ustb.edu.cn

通讯作者:
孙长银东南大学自动化学院教授.2001年和2003年分别获得东南大学自动化学院硕士和博士学位.主要研究方向为智能控制, 飞行器控制, 模式识别和优化理论.本文通信作者.E-mail:cysun@seu.edu.cn

计量
- 文章访问数: 2345
- HTML全文浏览量: 202
- PDF下载量: 767
- 被引次数: 0
出版历程
- 收稿日期: 2016-11-14
- 录用日期: 2017-02-03
- 刊出日期: 2017-08-20

Fault Diagnosis for Networked Systems By Asynchronous IMM Fusion Filtering

1.
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083
2.
School of Automation, Southeast University, Nanjing 210096

Funds:

National Natural Science Foundation of China 61520106009

National Natural Science Foundation of China 61533008

National Natural Science Foundation of China 61304105

More Information

Author Bio:
   Lecturer at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She received her Ph. D. degree from the Department of Automation, Tsinghua University in 2011. Her research interest covers fault diagnosis, fault prediction, and information fusion.E-mail:

   Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his bachelor degree from the School of Automation and Electrical Engineering, University of Science and Technology Beijing in 2013. His research interest covers estimation fusion, fault diagnosis and prediction, and event-triggered systems.E-mail:

    Master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She received her bachelor degree from Tianjin Normal University in 2015. Her research interest covers information fusion and fault diagnosis.E-mail:

Corresponding author: SUN Chang-Yin Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his master and Ph. D. degrees from the School of Automation, Southeast University in 2001 and 2003, respecttively. His research interest covers intelligent control, flight control, pattern recognition, and optimal theory. Corresponding author of this paper.E-mail:cysun@seu.edu.cn

摘要

摘要: 针对一类带随机丢包的异步多传感器网络化系统，提出了基于网络化异步交互式多模型（Interacting multiple model，IMM）融合滤波的故障诊断方法.考虑不同传感器通道具有不同丢包概率的情况，将未知的故障幅值看作扩维的系统状态，利用提出的网络化异步IMM融合滤波算法对由系统正常模型和各种可能的故障模型构成的模型集进行滤波，根据模型概率进行故障检测和定位，同时得到故障幅值和系统状态的联合估计.提出的方法避免了传统IMM故障诊断方法模型集设计中故障大小难以确定的问题，适用于具有任意采样速率和任意初始采样时刻的异步多传感器网络化系统，并且通过融合多个传感器的信息提高了故障诊断的准确性.仿真实例验证了所提出方法的可行性和有效性.
- 故障诊断 /
- 网络化系统 /
- 异步融合 /
- 交互式多模型滤波
Abstract: A fault diagnosis method is proposed based on networked asynchronous interacting multiple model (IMM) fusion filtering for a kind of networked systems with multiple asynchronous sensors and stochastic packet dropouts. The proposed networked asynchronous IMM fusion filtering algorithm is used to perform fusion filtering for the model set consisting of the normal model and all kinds of possible fault models of the system, where different arriving probabilities are considered for different sensor communication channels and the unknown fault amplitude is taken as the augmented system state. Fault detection and location are achieved based on the model possibilities, and the estimates of system state and fault amplitude can be obtained simultaneously. The proposed method avoids the problem of determining fault amplitude in the model set design of traditional IMM fault diagnosis approaches, improves the accuracy of fault diagnosis by fusing information from multiple sensors, and can be used to asynchronous multi-sensor networked systems with arbitrary sampling rates and arbitrary initial sampling time instants. The feasibility and effectiveness of the proposed algorithm are illustrated by simulation examples.
- Fault diagnosis /
- networked systems /
- asynchronous fusion /
- interacting multiple model (IMM) filtering
注释:

1) 本文责任编委文成林

HTML全文

图 1 融合区间$(t_{k-1}, t_k]$内异步多传感器网络化测量

Fig. 1 Networked measurements from asynchronous multi-sensors during fusion interval $(t_{k-1}, t_k]$

下载: 全尺寸图片幻灯片

图 2 模型后验概率曲线

Fig. 2 The posterior probability curves of models

下载: 全尺寸图片幻灯片

图 3 故障幅值的估计曲线

Fig. 3 Estimation curves of fault amplitude

下载: 全尺寸图片幻灯片

图 4 状态估计的均方根误差曲线

Fig. 4 RMSE curves of state estimation

下载: 全尺寸图片幻灯片

图 5 故障幅值估计的均方根误差曲线

Fig. 5 RMSE curves of fault amplitude

下载: 全尺寸图片幻灯片

参考文献(20)

[1]	鄢镕易, 何潇, 周东华.一类存在参数摄动的线性随机系统的鲁棒间歇故障诊断方法.自动化学报, 2016, 42(7):1004-1013 http://www.aas.net.cn/CN/abstract/abstract18891.shtml Yan Rong-Yi, He Xiao, Zhou Dong-Hua. Robust diagnosis of intermittent faults for linear stochastic systems subject to time-varying perturbations. Acta Automatica Sinica, 2016, 42(7):1004-1013 http://www.aas.net.cn/CN/abstract/abstract18891.shtml
[2]	徐晓滨, 张镇, 李世宝, 文成林.基于诊断证据静态融合与动态更新的故障诊断方法.自动化学报, 2016, 42(1):107-121 http://www.aas.net.cn/CN/abstract/abstract18800.shtml Xu Xiao-Bin, Zhang Zhen, Li Shi-Bao, Wen Cheng-Lin. Fault diagnosis based on fusion and updating of diagnosis evidence. Acta Automatica Sinica, 2016, 42(1):107-121 http://www.aas.net.cn/CN/abstract/abstract18800.shtml
[3]	Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V. Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4):1255-1321 doi: 10.1109/TAES.2005.1561886
[4]	Zhang Y M, Li X R. Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(4):1293-1313 doi: 10.1109/7.722715
[5]	Ru J F, Li X R. Interacting multiple model algorithm with maximum likelihood estimation for FDI. In:Proceedings of the 2003 IEEE International Symposium on Intelligent Control. Houston, TX, USA:IEEE, 2003. 661-666
[6]	Ru J F, Li X R. Variable-structure multiple-model approach to fault detection, identification, and estimation. IEEE Transactions on Control Systems Technology, 2008, 16(5):1029-1038 doi: 10.1109/TCST.2007.916318
[7]	Kim S, Choi J, Kim Y. Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3):940-952 doi: 10.1109/TAES.2008.4655354
[8]	Zhao S Y, Huang B, Liu F. Fault detection and diagnosis of multiple-model systems with Mismodeled transition probabilities. IEEE Transactions on Industrial Electronics, 2015, 62(8):5063-5071 doi: 10.1109/TIE.2015.2402112
[9]	Zhang Y M, Jiang J. Integrated active fault-tolerant control using IMM approach. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(4):1221-1235 doi: 10.1109/7.976961
[10]	Tudoroiu N, Khorasani K. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach. In:Proceedings of the 2005 IEEE Conference on Control Applications. Toronto, Canada:IEEE, 2005. 1287-1292
[11]	Judalet V, Glaser S, Gruyer D, Mammar S. IMM-based sensor fault detection and identification for a drive-by-wire vehicle. In:Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes. Paris, France:IFAC, 2015. 1158-1164
[12]	韩崇昭, 朱洪艳, 段战胜.多源信息融合.第2版.北京:清华大学出版社, 2010. 455-496 Han Chong-Zhao, Zhu Hong-Yan, Duan Zhan-Sheng. Multi-Source Information Fusion (2nd edition). Beijing:Tsinghua University Press, 2010. 455-496
[13]	潘泉.多源信息融合理论及应用.北京:清华大学出版社, 2013. 1 -21 Pan Quan. Multi-Source Information Fusion Theory and Its Applications. Beijing:Tsinghua University Press, 2013. 1-21
[14]	Liu W Y, Wei J, Liang M C, Cao Y, Hwang I. Multi-sensor fusion and fault detection using hybrid estimation for air traffic surveillance. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4):2323-2339 doi: 10.1109/TAES.2013.6621819
[15]	Hu Y Y, Zhou D H. Actuator fault diagnosis for dynamic systems with multiple asynchronous sensors using the IMM approach. In:Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation. Shanghai, China:IEEE, 2011. 318-321
[16]	He X, Wang Z D, Liu Y, Zhou D H. Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach. IEEE Transactions on Industrial Informatics, 2013, 9(3):1670-1679 doi: 10.1109/TII.2013.2251891
[17]	Dong H L, Wang Z D, Ding S X, Gao H J. On H_∞ estimation of randomly occurring faults for a class of nonlinear time-varying systems with fading channels. IEEE Transactions on Automatic Control, 2016, 61(2):479-484 doi: 10.1109/TAC.2015.2437526
[18]	He X, Wang Z D, Wang X F, Zhou D H. Networked strong tracking filtering with multiple packet dropouts:algorithms and applications. IEEE Transactions on Industrial Electronics, 2014, 61(3):1454-1463 doi: 10.1109/TIE.2013.2261038
[19]	Li X R. Optimal linear estimation fusion-part Ⅶ:dynamic systems. In:Proceedings of the 6th International Conference of Information Fusion. Cairns, Australia:IEEE, 2003. 455-462
[20]	彭开香, 张凯, 李钢.基于贡献率法的非线性工业过程在线故障诊断.自动化学报, 2014, 40(3):423-430 http://www.aas.net.cn/CN/abstract/abstract18307.shtml Peng Kai-Xiang, Zhang Kai, Li Gang. Online contribution rate based fault diagnosis for nonlinear industrial processes. Acta Automatica Sinica, 2014, 40(3):423-430 http://www.aas.net.cn/CN/abstract/abstract18307.shtml