基于改进距离相似度的故障可分离性分析方法

doi:10.6040/j.issn.1672-3961.0.2017.269

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 103-109.doi: 10.6040/j.issn.1672-3961.0.2017.269

基于改进距离相似度的故障可分离性分析方法

宋洋¹,钟麦英^2*

1. 北京航空航天大学仪器科学与光电工程学院, 北京 100191;2. 山东科技大学电气与自动化工程学院, 山东青岛 266590

收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
通讯作者: 钟麦英(1965— ),女,山东淄博人,教授,博士,主要研究方向为鲁棒故障诊断与容错控制. E-mail: myzhong@buaa.edu.cn E-mail:ysong@buaa.edu.cn
作者简介:宋洋(1990— ),女,吉林省集安人,博士,主要研究方向为故障诊断. E-mail: ysong@buaa.edu.cn
基金资助:
国家自然科学基金资助项目(61333005,61421063);山东省泰山学者优势特色学科人才资助项目

Fault isolability analysis based on improved distance similarity

SONG Yang¹, ZHONG Maiying^2*

1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China;
2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

摘要/Abstract

摘要： 为了定量分析故障分离的难易程度,提出基于改进距离相似度的故障可分离性分析方法。以基于等价空间的故障诊断残差产生器为例,根据不同故障引起残差向量在概率分布上的差异建立改进故障分离条件,利用残差向量的距离相似度和方向相似度评价故障分离的难易程度,提出基于改进距离相似度的故障可分离性定量评价指标。通过仿真试验分析某固定翼无人机纵向飞行控制系统的故障可分离性。结果表明:该方法能够准确判断故障分离条件,定量分析系统进行故障分离的难易程度,与现有方法相比,改进故障分离条件更为直观,基于改进距离相似度的故障可分离性评价指标能够实现残差向量在距离差异和方向差异的综合评价。

关键词: 故障诊断, 故障可分离性, 残差产生器, 定量评价, 改进距离相似度

Abstract: A fault isolability analysis approach based on improved distance similarity was proposed to evaluate quantitatively the difficulty level of fault isolation. A parity space-based fault diagnosis residual generator was taken as an example, and the improved fault isolation condition was constructed based on the difference of the residuals in probability distribution. Then the distance similarity and direction similarity of residuals were adopted to evaluate the difficulty level of fault isolation, and the quanlitative evaluation index of fault isolability was put forward. A simulation was carried out to analyze the fault isolability of a fixed-wing unmanned aerial vehicle longitudinal flight control system. The results demonstrated that the method could decide accurately the fault isolation condition, and evaluate quantitatively the difficulty level of fault isolation. The improved fault isolation condition was more intuitional, and the evaluation index could evaluate comprehensively the difference of residuals in both distance and direction compared with existing approaches.

Key words: residual generator, quantitative evaluation, fault diagnosis, improved distance similarity, fault isolability

中图分类号:

TP206

宋洋,钟麦英. 基于改进距离相似度的故障可分离性分析方法[J]. 山东大学学报(工学版), 2017, 47(5): 103-109.

SONG Yang, ZHONG Maiying. Fault isolability analysis based on improved distance similarity[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 103-109.

参考文献

[1] GERTLER J J. Fault detection and diagnosis in engineering systems[M]. New York: Marcel Dekker, 1998:188-191.
[2] DING S X. Model-based fault diagnosis techniques: design schemes, algorithms, and tools[M]. Berlin: Springer, 2013:98-102, 405-413.
[3] 闻新, 张洪钺, 周露. 控制系统的故障诊断和容错控制[M]. 北京: 机械工业出版社, 1998:31-37.
[4] GAO Z W, CECATI C, DING S X. A survey of fault diagnosis and fault-tolerant techniques:part I: fault diagnosis with model-based and signal-based approaches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757-3767.
[5] ZHONG M Y, DING S X, ZHOU D H. A new scheme of fault detection for linear discrete tme-varying systems[J]. IEEE Transactions on Automatic Control, 2016, 61(9): 2597-2602.
[6] HERRERA-OROZCO A R, BRETAS A S, OROZCO-HENAO C, et al. Incipient fault location formulation: a time-domain system model and parameter estimation approach[J]. International Journal of Electrical Power and Energy Systems, 2017, 90: 112-123.
[7] KOSCIELNY J M, BARTYS M, RZEPIEJEWSKI P, et al. Actuator fault distinguishability study for the DAMADICS benchmark problem[J]. Control Engineering Practice, 2006, 14(6): 645-665.
[8] SHARIFI R, LANGARI R. Isolability of faults in sensor fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25(7): 2733-2744.
[9] SHARIFI R, LANGARI R. Sensor fault diagnosis with a probabilistic decision process[J]. Mechanical Systems and Signal Processing, 2013, 34(1): 146-155.
[10] 李文博, 王大轶,刘成瑞. 动态系统实际故障可诊断性的量化评价研究[J]. 自动化学报, 2015, 41(3): 497-507. LI Wenbo, WANG Dayi, LIU Chengrui. Quantitative evaluation of actual fault diagnosability for dynamic systems[J]. ACTA Automatica Sinica, 2015, 41(3): 497-507.
[11] CHI C Y, WANG D W, YU M, et al. Sensor placement for fault isolability using low complexity dynamic programming[J]. IEEE Transaction on Automation Science and Engineering, 2015, 12(3): 1080-1090.
[12] 刘文静, 刘成瑞, 王南华, 等. 定量与定性相结合的动量轮故障可诊断性评价[J]. 中国空间科学技术, 2011(4):54-63. LIU Wenjing, LIU Chengrui, WANG Nanhua, et al. Qualitative and quantitative evaluation on fault diagnosability of momentum wheels[J]. Chinese Space Science and Technology, 2011(4):54-63.
[13] ERIKSSON D, FRISK E, KRYSANDER M. A method for quantitative fault diagnosability analysis of stochastic linear descriptor models[J]. Automatica, 2013, 49(1): 1591-1600.
[14] JUNG D, F RISK E, KRYSANDER M. Quantitative isolability analysis of different fault modes[J]. IFAC-Papers Online, 2015, 48(21):1275-1282.
[15] PARK S, P PARK Y, P PARK Y S. Degree of fault isolability and active fault diagnosis for redundantly actuated vehicle system[J]. International Journal of Automotive Technology, 2016, 17(6):1045-1053.
[16] KULLBACK S, LEIBLER R A. On information and sufficiency[J]. The Annals of Mathematical Statistics, 1951, 22(1):79-86.
[17] HAMERSMA H A, SCHALK E P. Longitudinal vehicle dynamic control for improved vehicle safety[J]. Journal of Terramechanics, 2014, 54: 19-36.
[18] QIAN M S, JIANG B, LIU H T. Dynamic surface active fault tolerant control design for the attitude control systems of UAV with actuator fault[J]. International Journal of Control Automation and Systems, 2016, 14(3): 723-732.
[19] HWANG W, HUH K. Fault detection and estimation for electromechanical brake systems using parity space approach[J]. Journal of Dynamic Systems. Measurement, & Control, 2015, 137(1): 1-7.
[20] ALIREZA A, KANG K Y, SHIRIN N, et al. Detection of fault data injection attack on UAV using adaptive neural network[J]. Procedia Computer Science, 2016, 95: 193-200.

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基于改进距离相似度的故障可分离性分析方法

Fault isolability analysis based on improved distance similarity

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