考虑突变状态检测的齿轮实时剩余寿命预测

摘要
图/表
参考文献(23)
相关文章 (15)

全文: PDF (1980 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为解决齿轮疲劳退化过程中状态突变后剩余寿命难以准确预测问题，提出一种考虑退化突变点检测与剩余寿命预测相关联的齿轮疲劳实时剩余寿命预测新方法。首先针对齿轮磨损退化过程建立状态空间预测模型，利用接收到的齿轮实时监测振动信息实时更新模型参数，同时对退化过程中的突变状态点进行检测，并根据突变点所提供的寿命信息采用卡尔曼前向滤波及平滑算法结合期望最大化参数估计算法在滤波的同时不断对状态空间模型参数进行修正，改变退化突变后的滤波效果，进行实时状态预测与寿命估计。运用齿轮疲劳寿命试验台的实时监测数据对预测模型进行验证，结果表明利用突变点信息对预测模型进行修正后可以更快的对系统的动态变化进行跟踪，提高预测齿轮退化状态及实时剩余寿命的准确度。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

关键词 ：剩余寿命预测, 状态空间建模, 卡尔曼滤波, 突变状态检测, 模型修正

Abstract：In order to solve the gear contact fatigue remaining useful life in the degradation process is difficult to accurately predict problems, a new method of real-time gear contact fatigue remaining useful life prediction is put forward that is a kind of integrated studies of abrupt change detection and remaining life prediction. Firstly, the state-space modelling for predicting degradation states of gear wear was established by using the real time monitoring vibration information received to update the model parameters. Using Kalman forward filtering and smoothing algorithm combined with parameter estimation of expectation–maximization algorithm, the prediction model was modified to change the filtering effect according to the life information of abrupt change detection provided. Using real-time monitoring data of the contact fatigue life of gear test rig to verify this model, the results show that a revised prediction model using abrupt point information can be faster to dynamic tracking system, improve the accuracy of gear degradation state and the real-time remaining useful life prediction.

Key words： Remaining useful life prediction State-space models Kalman filtering Abrupt change detection Model correction

收稿日期: 2016-01-11 出版日期: 2017-10-28

引用本文:

石慧1，曾建潮1,2. 考虑突变状态检测的齿轮实时剩余寿命预测[J]. 振动与冲击, 2017, 36(21): 173-184.
SHI Hui1 ZENG Jianchao1,2. A Model for Real Time Remaining Useful Life Prediction of Gear Based on Abrupt Change Detection. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(21): 173-184.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I21/173

[1] 冯志鹏, 秦嗣峰. 基于 Hilbert 振动分解和高阶能量算子的行星齿轮箱故障诊断研究[J]. 振动与冲击, 2016, 35(5): 47-54. [Feng Zhipeng,Qin Sifeng. Planetary gearbox fault diagnosis based on hilbert vibration decomposition and higher order differential energy operator [J]. Journal of Vibration and Shock, 2016, 35(5): 47-54.]
[2] X. Si, W. Wang, M. Chen, C. Hu, D. Zhou. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution[J]. European journal of operational research, 2013, 226(1): 53-66.
[3] D. Vališ, L. Žák, O. Pokora. Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2015, 229(1): 36-45.
[4] 孙强, 岳继光. 基于不确定性的故障预测方法综述[J]. 控制与决策, 2014, 29(5): 769-778. [SUN Qiang, YUE Ji-guang. Review on fault prognostic methods based on uncertainty[J]. Control and Decision2014, 29(5): 769-778.]
[5] M. Gašperin, Đ. Juričić, P. Boškoski, J. Vižintin. Model-based prognostics of gear health using stochastic dynamical models[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 537-548.
[6] M. J. Carr, W. Wang. An approximate algorithm for prognostic modelling using condition monitoring information[J]. European Journal of Operational Research, 2011, 211(1): 90-96.
[7] J. Sun, H. Zuo, W. Wang, M. G. Pecht. Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance[J]. Mechanical Systems and Signal Processing, 2012, 28(0): 585-596.
[8] Bressel M, Hilairet M, Hissel D. Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell[J]. Applied Energy, 2016, 164: 220-227.
[9] D. Tang, V. Makis, L. Jafari, J. Yu. Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring[J]. Reliability Engineering & System Safety, 2015, 134: 198-207.
[10] P. Lall, R. Lowe, K. Goebel. Extended Kalman filter models and resistance spectroscopy for prognostication and health monitoring of leadfree electronics under vibration[J]. Reliability, IEEE Transactions on, 2012, 61(4): 858-871.
[11] W. Wang. A model to predict the residual life of rolling element bearings given monitored condition information to date[J]. IMA Journal of Management Mathematics, 2002, 13(1): 3-16.
[12] 胡昌华, 王志远, 周志杰. 基于随机滤波理论的剩余寿命预测方法研究[J]. 系统仿真技术, 2011, 7(2): 83-88. [Hu Changhua, Wang Zhiyuan, Zhou Zhijie. A Study on residual life prediction method based on stochastic filtering theory[J]. System Simulation Technology, 2011,7 (4):83-88.]
[13] 李鑫, 吕琛, 王自力, 陶小创. 考虑退化模式动态转移的健康状态自适应预测[J]. 自动化学报, 2014, 40(9): 1889-1895. [Li Xin, Lv Chen, Wang Zili, et.al. Self-adaptive health condition prediction considering dynamic transfer of degradation mode[J] Acta Automatica Sinica, 2014, 40(9):1889-1895.]
[14] 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[J]. 自动化学报, 2013, 39(6): 711-722. [ZHOU Donghua, WEI Muheng, SI Xiaosheng. A survey on anomaly detection, life prediction and maintenance decision for industrial processes[J]. Acta Automatica Sinica, 2013, 39(6): 711-722.]
[15] J. Son, Y. Zhang, C. Sankavaram, S. Zhou. RUL Prediction for Individual Units Based on Condition Monitoring Signals With a Change Point[J]. Reliability, IEEE Transactions on, 2015, 64(1): 182-196.
[16] 刘亚琼, 王铁, 武志斐, 周驰, 马勇. QC 商用车变速器疲劳试验振动分析[J]. 机械传动, 2014, 38(7): 104-105. [Liu Yaqiong,Wang Tie, Wu Zhifei, Zhou Chi, Ma Yong. Vibration analysis of QC commercial vehicle transmission fatigue test[J]. mechanical drive, 2014, 7(38): 104-105.]
[17] J. A. Hines, W. D. Mark. Bending-fatigue damage-detection on notched-tooth spiral-bevel gears using the average-log-ratio, ALR, algorithm[J]. Mechanical Systems and Signal Processing, 2014, 43(1): 44-56.
[18] J. Unnikrishnan, V. V. Veeravalli, S. P. Meyn. Minimax robust quickest change detection[J]. Information Theory, IEEE Transactions on, 2011, 57(3): 1604-1614.
[19] 伊廷华, 郭庆, 李宏男. 基于控制图的 GPS 异常监测数据检验方法研究[J]. 工程力学, 2013, 30(8): 133-141. [Yi Tinghua, Guo Qing, Li Hongnan. The research on detection methods of gps abnormal monitoring data based on control chart[J].Engineering Mechanics, 2013,30(8):133-141.]
[20] D. Siegmund. Error probabilities and average sample number of the sequential probability ratio test[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1975: 394-401.
[21] W. Wang, M. Carr, W. Xu, K. Kobbacy. A model for residual life prediction based on Brownian motion with an adaptive drift[J]. Microelectronics Reliability, 2011, 51(2): 285-293.
[22] M. E. Khan, D. N. Dutt. An expectation-maximization algorithm based kalman smoother approach for event-related desynchronization (ERD) estimation from EEG[J]. Biomedical Engineering, IEEE Transactions on, 2007, 54(7): 1191-1198.
[23] C. Lu, L. Tao, H. Fan. An intelligent approach to machine component health prognostics by utilizing only truncated histories[J]. Mechanical Systems and Signal Processing, 2014, 42(1): 300-313.