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基于非线性故障重构的旋转机械故障预测方法

马洁 李钢 陈默

马洁, 李钢, 陈默. 基于非线性故障重构的旋转机械故障预测方法. 自动化学报, 2014, 40(9): 2045-2049. doi: 10.3724/SP.J.1004.2014.02045
引用本文: 马洁, 李钢, 陈默. 基于非线性故障重构的旋转机械故障预测方法. 自动化学报, 2014, 40(9): 2045-2049. doi: 10.3724/SP.J.1004.2014.02045
MA Jie, LI Gang, CHEN Mo. Nonlinear Fault Reconstruction Based Fault Prognosis for Rotating Machinery. ACTA AUTOMATICA SINICA, 2014, 40(9): 2045-2049. doi: 10.3724/SP.J.1004.2014.02045
Citation: MA Jie, LI Gang, CHEN Mo. Nonlinear Fault Reconstruction Based Fault Prognosis for Rotating Machinery. ACTA AUTOMATICA SINICA, 2014, 40(9): 2045-2049. doi: 10.3724/SP.J.1004.2014.02045

基于非线性故障重构的旋转机械故障预测方法

doi: 10.3724/SP.J.1004.2014.02045
基金项目: 

国家自然科学基金(61273173),北京市自然科学基金(4122029)资助

详细信息
    作者简介:

    马洁 北京信息科技大学自动化学院教授.主要研究方向为数据驱动的过程监控,故障预测.本文通信作者.E-mail:mjbeijing@163.com

    通讯作者:

    马洁 北京信息科技大学自动化学院教授.主要研究方向为数据驱动的过程监控,故障预测.本文通信作者.E-mail:mjbeijing@163.com

Nonlinear Fault Reconstruction Based Fault Prognosis for Rotating Machinery

Funds: 

Supported by National Natural Science Foundation of China (61273173), and Natural Science Foundation of Beijing (4122029)

  • 摘要: 对旋转机械的状态进行在线监测和故障预测是一个具有重要应用价值的工程问题. 采用基于核主元分析的非线性故障重构技术研究了多变量相关条件下旋转机械的故障估计及预测问题. 首先利用核主元分析对旋转机械系统进行离线非线性建模,并进行异常检测. 通过对故障程度进行定量描述,用最优化方法求解故障重构意义下的故障估计;然后 用多层递阶的方法对估计出的故障幅值的发展趋势进行预测. 最后,以中国石化北京燕山分公司的烟气轮机作为实际应用对象,验证了该方法的有效性.
  • [1] Wen Bang-Chun, Wu Xin-Hua, Ding Qian, Han Qing-Kai. Theory and Experiments of Nonlinear Dynamics for Faulty Rotating Machinery. Beijing: Science Press, 2004. (闻邦椿, 武新华, 丁千, 韩清凯. 故障旋转机械非线性动力学的理论与试验. 北京: 科学出版社, 2004.)
    [2] Ma Jie, Xu Xiao-Li, Zhou Dong-Hua. Survey of fault predication methods for rotating machineries. Process Automatica Instrumentation, 2011, 32(8): 1-3 (马洁, 徐小力, 周东华. 旋转机械的故障预测方法综述. 自动化仪表, 2011, 32(8): 1-3)
    [3] 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)
    [4] Zhou Dong-Hua, Chen Mao-Yin, Xu Zheng-Guo. The Reliability Prediction and Optimal Maintenance Technology. Hefei: Press of University of Science and Technology of China, 2013.(周东华, 陈茂银, 徐正国. 可靠性预测与最优维护技术. 合肥: 中国科学技术大学出版社, 2013.)
    [5] Fan Ji-Cong, Wang You-Qing, Qin Si-Zhao. Combined indices for ICA and their applications to multi-variate process fault diagnosis. Acta Automatica Sinica, 2013, 39(5): 494-501(樊继聪, 王友清, 秦泗钊. 联合指标独立成分分析在多变量过程故障诊断中的应用. 自动化学报, 2013, 39(5): 494-501)
    [6] Zhou Dong-Hua, Liu Yang, He Xiao. Review on fault diagnosis techniques for closed-loop systems. Acta Automatica Sinica, 2013, 39(11): 1933-1943(周东华, 刘洋, 何潇. 闭环系统故障诊断技术综述. 自动化学报, 2013, 39(11): 1933-1943)
    [7] Zhou Dong-Hua, Shi Jian-Tao, He Xiao. Review of intermittent fault diagnosis techniques for dynamic systems. Acta Automatica Sinica, 2014, 40(2): 161-171(周东华, 史建涛, 何潇. 动态系统间歇故障诊断技术综述. 自动化学报, 2014, 40(2): 161-171)
    [8] Zhou Dong-Hua, Wei Mu-Heng, Si Xiao-Sheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes. Acta Automatica Sinica, 2013, 39(6): 711-722(周东华, 魏慕恒, 司小胜. 工业过程异常检测, 寿命预测与维修决策的研究进展. 自动化学报, 2013, 39(6): 711-722)
    [9] Si Xiao-Sheng, Hu Chang-Hua, Zhou Dong-Hua. Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error. Acta Automatica Sinica, 2013, 39(5): 530-541(司小胜, 胡昌华, 周东华. 带测量误差的非线性退化过程建模与剩余寿命估计. 自动化学报, 2013, 39(5): 530-541)
    [10] Heng A, Zhang S, Tan A C C, Mathew J. Rotating machinery prognostics: state of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 2009, 23(3): 724-739
    [11] Li C J, Lee H. Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 2005, 19(4): 836-846
    [12] Kacprzynaki G J, Sarlashkar A, Roemer M J, Hess A, Hardman B. Predicting remaining life by fusing the physics of failure modeling with diagnostics. Joural of the Minerals Metals and Materials Society, 2004, 56(3): 29-35
    [13] Qiu J, Set B B, Liang S Y, Zhang C. Damage mechanics approach for bearing lifetime prognostics. Mechanical Systems and Signal Processing, 2002, 16(5): 817-829
    [14] Wang W. An adaptive predictor for dynamic system forecasting. Mechanical Systems and Signal Processing, 2007, 21(2): 809-823
    [15] Dong M, He D. A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing, 2007, 21(5): 2248-2266
    [16] Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006, 20(7): 1483-1510
    [17] Sun Y, Ma L, Mathew J, Wang W, Zhang S. Mechanical systems hazard estimation using condition monitoring. Mechanical Systems and Signal Processing, 2006, 20(5): 1189-1201
    [18] Wang W B, Zhang W J. A model to predict the residual life of aircraft engines based upon oil analysis data. Naval Research Logistics, 2005, 52(3): 276-284
    [19] Si X S, Wang W B, Hu C H, Zhou D H, Pecht M G. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Transactions on Reliability, 2012, 61(1): 50-67
    [20] Si X S, Wang W, Hu C H, Chen M Y, Zhou D H. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing, 2013, 35(1-2): 219-237
    [21] Si X S, Wang W, Hu C H, Zhou D H. Remaining useful life estimation——a review on the statistical data driven approaches. European Journal of Operational Research, 2011, 213(1): 1-14
    [22] Wei M H, Chen M Y, Zhou D H. Multi-sensor information based remaining useful life prediction with anticipated performance. IEEE Transactions on Reliability, 2013, 62(1): 183-198
    [23] Xu Z G, Ji Y D, Zhou D H. Real-time reliability prediction for a dynamic system based on the hidden degradation process identification. IEEE Transactions on Reliability, 2008, 57(2): 230-242
    [24] Li G, Qin S J, Ji Y D, Zhou D H. Reconstruction based fault prognosis for continuous processes. Control Engineering Practice, 2010, 18(10): 1211-1219
    [25] Schölkopf B, Smola A, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5): 1299-1319
    [26] Lee J M, Yoo C K, Choi S W, Vanrolleghem P A, Lee I B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 2004, 59(1): 223-234
    [27] Dunia R, Qin S J. Subspace approach to multi-dimensional fault identification and reconstruction. AIChE Journal, 1998, 44(8): 1813-1831
    [28] Alcala C F, Qin S J. Reconstruction based contribution for process monitoring with kernel principal component analysis. Industrial & Engineering Chemistry Research, 2010, 49(17): 7849-7857
    [29] Han Zhi-Gang. A new method of dynamic system prediction. Acta Automatica Sinica, 1983, 9(3): 161-168 (韩志刚. 动态系统预报的一种新方法. 自动化学报, 1983, 9(3): 161-168)
    [30] Chen Mo, Ma Jie. Nonlinear fault prognosis for stack gas turbine machine based on KPCA-MLR. Journal of Beijing Information Science and Technology University, 2013, 28(1): 30-35(陈默, 马洁. 基于KPCA-MLR的烟气轮机非线性故障预测. 北京信息科技大学学报, 2013, 28(1): 30-35)
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出版历程
  • 收稿日期:  2013-06-14
  • 修回日期:  2014-02-26
  • 刊出日期:  2014-09-20

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