基于DTW匹配的粒子滤波故障预报
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福建省自然科学基金(2013J01223);国家自然科学基金(61175123);福建师范大学"网络与信息安全关键理论和技术"校创新团队(IRTL1207)


Particle Filter Fault Prediction Based on Dynamic Time Warping Match
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    摘要:

    现有的粒子滤波故障预报方法主要是通过粒子滤波算法得到对应时刻的预测值,然后比较预测序列与观测序列的距离来对故障进行预报,但这种基于相同长度时间序列的故障预报方法不能处理预测序列与观测序列长度不同的情况.本文借助适用于不同长度时间序列的动态时间弯曲技术,对故障相关的时间序列进行分析,从动态时间弯曲算法度量设备正常工作的时间序列与潜在故障引起的异常序列之间相似度的角度,设计了系统正常度及反常度来判别设备是否正常运行,进而对潜在故障进行预报.实验结果验证了该方法的可行性,并能及时准确地预报出系统故障.

    Abstract:

    The existing particle filter fault prediction methods give the predictive value of the corresponding time by the particle filter algorithm, and then compare the distance between forecasting sequence and observation sequence to predict the fault. However, this fault prediction method can not handle the condition that the length of forecasting sequence is different from that of observation sequence. Dynamic Time Warping is a pattern matching algorithm based on nonlinear, which is suitable for the time sequence of different lengths. This paper is from the new perspective of using the Dynamic Time Warping algorithm to measure the similarity between normal working equipment's time sequence and abnormal sequence caused by potential faults, and design the system normal degree and abnormal degree to distinguish whether the device is operating properly or not, thus predict potential faults. Experimental results demonstrate the feasibility of this method, which can predict the system faults timely and accurately.

    参考文献
    1 Sun B, Kang R, Xie JS. Research and application of the prognostic and health management system. System Engineering and Electronics, 2007, 29(10):1762-1767
    2 景博,黄以锋,张建业.航空电子系统故障预测与健康管理技术现状与发展.空军工程大学学报(自然科学版),2010, 11(6):1-6.
    3 杨占才,安茂春,王红.对发展故障预测和健康管理技术的探讨.测控技术,2012,31(11).
    4 曾声奎, Pecht MG,吴际.故障预测与健康管理(PHM)技术的现状与发展.航空学报,2005,5:626-632.
    5 刘志仓.基于粒子滤波的非线性系统故障诊断与预测方法研究[硕士学位论文].西安:西安电子科技大学, 2013
    6 Das Sarma A, Benjelloun O, Halevy A, Widom J. Working models for uncertain data. Proc. of ICDE. 2006.
    7 胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005, 20(4):362-365,371.
    8 Chan KP, et al. Haar wavelets for efficient similarity search of time-series:With and without time wraping. IEEE TKDE, 2003:686-705.
    9 Kim DY. Moongu jeon:Spatio-temporal auxiliary particle filtering with L1-norm-based appearance model learning for robust visual tracking. IEEE Trans. on Image Processing, 2013, 22(2):511-522.
    10 Salvador S, Chan P. Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 2007, 11(5):561-580.
    11 李俊奎,时间序列相似性问题研究[博士学位论文].武汉:华中科技大学,2008.
    12 张琪,胡昌华,乔玉坤,蔡艳宁.基于随机摄动粒子滤波器的故障预报算法.控制与决策,2009,24(2):284-288.
    13 Orchard M, Vachtsevanos G. A particle filtering approach for on-line fault diagnosis and failure prognosis. Trans. of the Institute of Measurement and Control, 2009, 31(3-4):221-246.
    14 Keogh E. Exact indexing of dynamic time wraping. VLDB. 2002. 406-417
    15 Kim SW, et al. An index-based approach for similarity search supporting time wraping in large sequence database. ICDE. 2001. 607-614
    16 Vaswani N, Rathi Y, Yezzi A, Tannenbaum A. Tracking deforming objects using particle filtering for geometric active contours. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2007,29(8).
    17 Arulampalam MS, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 2002, 50(2):174-188.
    18 Ristic B, Arulampalam S, Gordon N. Beyond the Kalman Filter:Particle Filters for Tracking Applications. Artech House, 2004.
    19 Liu JS, Chen R. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association(Taylor & Francis Group), 1998, 93(443):1032-1044
    20 Djuric PM, Kotecha JH, Zhang JQ, et al. Particle filtering.IEEE Signal Processing Magazine, 2003, 20(5):19-38.
    21 Mo YW, Xiao DY. Evolutionary particle filter and its application. Control Theory and Application, 2005, 22(2):269-270.
    22 Chang C, Ansari R. Kernel particle filter for visual tracking. IEEE Signal Processing Letters, 2005, 12(3):242-245.
    23 Xu Z, Ji Y, Zhou D. A new real-time reliability prediction method for dynamic systems based on on-line fault prediction. IEEE Trans. on Reliability, 2009, 58(3):523-538.
    24 Orchard M, Vachtsevanos G. A particle filtering approach for on-line fault diagnosis and failure prognosis. Trans. of the Institute of Measurement and Control, 2009, 31(3-4):221-246.
    25 Freitas ND. Rao-Blackwellised particle filtering for fault diagnosis. IEEE Aerospace Conference Proceedings, 2002, 4:1767-1772.
    26 Xie XQ, Zhou DH, Jin YH. Strong tracking filter based adaptive generic model control. Journal of Process Control, 1999, 9(4):337-350.
    27 Doucet A, Godsill SJ, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 2000, 10(3):197-208.
    28 Tao W, Huang YF, Chen P. Particle filtering for adaptive sensor fault detection and identification. Proc. of the 2006 IEEE Int Conf on Robotics and Automation. Orlando, 2006:3807-3812.
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蒋欣,王开军,陈黎飞.基于DTW匹配的粒子滤波故障预报.计算机系统应用,2016,25(3):124-130

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  • 收稿日期:2015-02-11
  • 最后修改日期:2015-04-26
  • 在线发布日期: 2016-03-17
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