一种基于最优未知输入观测器的故障诊断方法

胡志坤; 孙岩; 姜斌; 何静; 张昌凡

doi:10.3724/SP.J.1004.2013.01225

一种基于最优未知输入观测器的故障诊断方法

doi: 10.3724/SP.J.1004.2013.01225

胡志坤^1,2,
孙岩¹,
姜斌²,
何静³,
张昌凡³

1.
中南大学物理与电子学院长沙 410083;
2.
南京航空航天大学自动化学院南京 210016;
3.
湖南工业大学电气工程学院株洲 412001

基金项目:

国家自然科学基金(61273159, 61104024, 60904077);中国博士后科学基金(2012M511752)资助

详细信息

作者简介:
孙岩中南大学物理与电子学院硕士研究生. 主要研究方向为计算机测控技术与故障诊断.E-mail: 349507785@163.com

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出版历程
- 收稿日期: 2012-05-16
- 修回日期: 2012-09-29
- 刊出日期: 2013-08-20

An Optimal Unknown Input Observer Based Fault Diagnosis Method

1.
School of Physics and Electronics, Central South University, Changsha 410083;
2.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016;
3.
School of Electrics Engineering, Hunan University of Technology, Zhuzhou 412001

Funds:

Supported by National Natural Science Foundation of China (61273159, 61104024, 60904077), China Postdoctoral Science Foundation (2012M511752)

摘要

摘要: 针对含有未知输入干扰和噪音的不确定动态系统,使用全阶未知输入观测器(Unknown input observer, UIO)来消除干扰项,实现状态估计, 结合Kalman滤波器算法来求解状态反馈矩阵,以使得输出残差信号的协方差最小,从而增强系统对噪声的鲁棒性,实现了一种基于最优未知输入观测器的残差产生器.采用极大似然比(Generalized likelihood ratio, GLR)的方法对残差信号进行评估,通过设定的阈值来提高诊断率. 最后以风力发电机组传动系统出现加性传感器故障和乘性传感器故障为例, 进行了残差信号的仿真,仿真结果说明了该方法的有效性.
- 故障诊断 /
- 未知输入观测器 /
- Kalman滤波器 /
- 极大似然比
Abstract: A full-order unknown input observer (UIO) is employed for uncertain dynamic systems with unknown input interference and noise to eliminate the interference and achieve state estimation, combine with the Kalman filter algorithm to solve the state feedback matrix to minimum the covariance of the residual signal, so as to enhance the robustness of the system noise, thus an optimal unknown input observer is achieved as a residual generator. The threshold is designed based on the generalized likelihood ratio (GLR) method to evaluate the residual signals and achieve a high fault detection rate. Finally, the drive train system of the wind turbine with additive sensor faults and multiplicative sensor faults is used as an example. The residual signals are simulated and the results shows the effectiveness of the proposed method.
- Fault detection /
- unknown input observer (UIO) /
- Kalman filter /
- generalized likelihood ratio (GLR)

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参考文献(18)

[1]	Ding S X. Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Berlin: Springer, 2008. 10-343
[2]	Zhou Dong-Hua, Hu Yan-Yan. Fault diagnosis techniques for dynamic systems. Acta Automatica Sinica, 2009, 35(6): 748-758(周东华, 胡艳艳. 动态系统的故障诊断技术. 自动化学报, 2009, 35(6): 748-758)
[3]	Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri S N. A review of process fault detection and diagnosis, Part I: quantitative model-based methods. Computers and Chemical Engineering, 2003, 27(3): 293-311
[4]	Patton R J, Chen J, Nielsen S B. Model-based methods for fault diagnosis: some guide-lines. Transactions on the Institute of Measurement and Control, 1995, 17(2): 73-83
[5]	Zhou Dong-Hua, Ye Hao, Wang Gui-Zeng. Discussion of some important issues of observer based fault diagnosis technique. Acta Automatica Sinica, 1998, 24(3): 338-344(周东华, 叶昊, 王桂增. 基于观测器方法的故障诊断技术若干重要问题的探讨. 自动化学报, 1998, 24(3): 338-344)
[6]	Chen J, Patton R J. Robust Model-based Fault Diagnosis for Dynamic Systems. London: Kluwer Academic Publishers, 1999. 10-200
[7]	Ding X C, Frank P M. Fault detection via factorization approach. Systems & Control Letters, 1990, 14(5): 431-436
[8]	Frank P M, Ding X C. Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis. Automatica, 1994, 30(5): 789-904
[9]	Massoumnia M A. A geometric approach to the synthesis of failure detection filters. IEEE Transactions on Automatic Control, 1986, 31(9): 839-846
[10]	Chow E Y, Willsky A S. Analytical redundancy and the design of robust failure detection system. IEEE Transactions on Automatic Control, 1984, 29(7): 603-614
[11]	Frisk E, Nyberg M. A minimal polynomial basis solution to residual generation for fault diagnosis in linear systems. Automatica, 2001, 37(9): 1417-1424
[12]	Li Zhen-Ying, Shen Yi, Hu Heng-Zhang. Design of observers for system with unknown inputs. Acta Aeronautica Et Astronautica Sinica, 2000, 21(5): 471-473(李振营, 沈毅, 胡恒章. 具有未知输入干扰的观测器设计. 航空学报, 2000, 21(5): 471-473)
[13]	Wang H, Daley S. Actuator fault diagnosis: an adaptive observer-based technique. IEEE Transactions on Automatics Control, 1996, 41(7): 1073-1078
[14]	Jiang Bin, Mao Ze-Hui, Yang Hao, Zhang You-Min. Fault Diagnosis and Fault Regulation of Control System. Beijing: National Defence Industry Press, 2009. 51-153 (姜斌, 冒泽慧, 杨浩, 张友民. 控制系统的故障诊断与故障调节. 北京: 国防工业出版社, 2009. 51-153)
[15]	Odgaard P F, Stoustrup J. Unknown input observer based scheme for detecting faults in a wind turbine converter. In: Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. Barcelona, Spain: IFAC, 2009. 162-166
[16]	Odgaard P F, Stoustrup J, Nielsen R. Observer based detection of sensor faults in wind turbines. In: Proceedings of the 2009 European Wind Energy Conference. Marseille, France, 2009. 1-10
[17]	Odgaard P F, Stousturp J, Kinnaert M. Fault tolerant control of wind turbines—a benchmark model. In: Proceedings of the 7th IFAC Symposium on Fault Detection Supervision and Safety of Technical Processes. Barcelona, Spain: IFAC, 2009. 155-160
[18]	Seiler P, Bokor J, Vanek B, Balas G J. Robust model matching for geometric fault detection filters. In: Proceedings of the 2011 American Control Conference. San Francisco, USA: IEEE, 2011. 226-231