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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 51-56.doi: 10.6040/j.issn.1672-3961.0.2017.238

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基于稀疏表示的间歇故障检测方法及仿真

杨瑞   

  1. 山东科技大学电气与自动化工程学院, 山东 青岛 266500
  • 收稿日期:2017-04-18 出版日期:2017-10-20 发布日期:2017-04-18
  • 作者简介:杨瑞(1985— ),男,山东济南人,讲师,博士,主要研究方向为机电自动化及故障诊断. E-mail: eleyangr@sdust.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61603223);山东省自然科学基金资助项目(ZR2016FB01);青岛市应用基础研究计划资助项目(16-5-1-7-jch,17-1-1-1-jch)

Intermittent fault detection method based on sparse representation

YANG Rui   

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266500, Shandong, China
  • Received:2017-04-18 Online:2017-10-20 Published:2017-04-18

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摘要： 基于间歇故障在某些域的稀疏性,提出一种基于稀疏表示的间歇故障检测方法。利用系统输出数据构造系统的过完备字典,设计间歇故障检测阈值,并对过完备字典和故障检测阈值进行在线更新。此方法适合动态系统的间歇故障检测,并通过仿真结果进行验证,并比较在不同更新策略下的仿真结果。

关键词: 间歇故障, 稀疏表示, 故障检测

Abstract: Based on the sparsity of intermittent faults in some domains, an intermittent fault detection method based on sparse representation was proposed. The system output data were used to build the overcomplete dictionary and design the fault detection threshold of intermittent fault, which was able to update the over-complete dictionary and fault detection threshold with online measurements. With the simulation verification, the proposed method was suitable for intermittent fault detection in dynamic system and results under different online updating strategies were compared.

Key words: fault detection, intermittent fault, sparse representation

中图分类号: 

  • TQ35
[1] MEHRA R K, PESCHON J. An innovations approach to fault detection and diagnosis in dynamic systems[J]. Automatica, 1971, 7(5): 637-640.
[2] ZHONG M, ZHOU D, DING S X. On designing fault detection filter for linear discrete time-varying systems[J]. IEEE Transactions on Automatic Control, 2010, 55(7): 1689-1695.
[3] 周东华, 史建涛, 何潇. 动态系统间歇故障诊断技术综述[J]. 自动化学报, 2014, 40(2): 161-171. ZHOU Donghua, SHI Jiantao, HE Xiang. Review of intermittent fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2014, 40(2): 161-171.
[4] CORRECHER A, GARCÍA E, MORANT F, et al. Intermittent failure dynamics characterization[J]. IEEE Transactions on Reliability, 2012, 61(3): 649-658.
[5] 周东华, 胡艳艳. 动态系统的故障诊断技术[J]. 自动化学报, 2009, 35(6): 748-758. ZHOU Donghua, HU Yanyan. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6): 748-758.
[6] CANDES E J, ROMBERG J K, TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.
[7] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[8] 栗茂林, 梁霖, 王孙安. 基于稀疏表示的故障敏感特征提取方法[J]. 机械工程学报, 2013, 49(1): 73-80. LI Maolin, LIANG Lin, WANG Sun'an. Sensitive feature extraction of machine faults based on sparse representation[J]. Journal of Mechanical Engineering, 2013, 49(1): 73-80.
[9] CHAKRABORTY S, CHATTERJEE A, GOSWAMI S K. A sparse representation based approach for recognition of power system transients[J]. Engineering Applications of Artificial Intelligence, 2014, 30: 137-144.
[10] 张晗, 杜朝辉, 方作为, 等. 基于稀疏分解理论的航空发动机轴承故障诊断 [J]. 机械工程学报, 2015, 51(1): 97-105. ZHANG Han, DU Zhaohui, FANG Zuowei, et al. Sparse decomposition based aero-engine's bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2015, 51(1): 97-105.
[11] CHEN X, DU Z, LI J, et al. Compressed sensing based on dictionary learning for extracting impulse components[J]. Journal of Mechanical Engineering Signal Processing, 2014, 96:94-109.
[12] TANG G, HOU W, WANG H, et al. Compressive sensing of roller bearing faults via harmonic detection from under-sampled vibration signals[J]. Sensors, 2015, 15(10): 25648-25662.
[13] 彭富强, 于德介, 罗洁思, 等. 基于多尺度线调频基稀疏信号分解的轴承故障诊断[J]. 机械工程学报, 2010, 46(7): 88-95. PENG Fuqiang, YU Dejie, LUO Jiesi, et al. Sparse signal decomposition method based on multi-scale chirplet and its application to bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2010, 46(7):88-95.
[14] WANG Y, XIANG J, MO Q, et al. Compressed sparse time—frequency feature representation via compressive sensing and its applications in fault diagnosis[J]. Measurement, 2015, 68: 70-81.
[15] 李娟, 周东华, 司小胜, 等. 微小故障诊断方法综述[J]. 控制理论与应用, 2012, 29(12):1517-1529. LI Juan, ZHOU Donghua, SI Xiaosheng, et al. Review of incipient fault diagnosis methods[J]. Control Theory & Applications, 2012, 29(12):1517-1529.
[16] 王宏超, 陈进, 董广明. 基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J]. 机械工程学报, 2013, 49(1): 88-94. WANG Hongchao, CHEN Jin, DONG Guangming. Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition[J]. Journal of Mechanical Engineering, 2013, 49(1):88-94.
[17] TANG H, CHEN J, DONG G. Sparse representation based latent components analysis for machinery weak fault detection [J]. Mechanical Systems and Signal Processing, 2014, 46(2):373-388.
[18] HE X, HU Y, PENG K. Intermittent fault detection for uncertain networked systems[J]. Mathematical Problems in Engineering, 2013, 2013(1):1-10.
[19] TAO Y, SHEN D, FANG M, et al. Reliable H∞ control of discrete-time systems against random intermittent faults[J]. International Journal of Systems Science, 2016, 47(10): 2290-2301.
[20] TAO Y, SHEN D, WANG Y, et al. Reliable H∞ control for uncertain nonlinear discrete-time systems subject to multiple intermittent faults in sensors and/or actuators [J]. Journal of the Franklin Institute, 2015, 352(11): 4721-4740.
[21] DENG G, QIU J, LIU G, et al. A novel fault diagnosis approach based on environmental stress level evaluation [J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2013, 227(5): 816-826.
[22] CORRECHER A, GARCÍA E, MORANT F, et al. Intermittent failure dynamics characterization[J]. IEEE transactions on reliability, 2012, 61(3): 649-658.
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