| |
摘要实时准确的剩余寿命预测能够为惯性测量组合的维护策略安排提供有效的决策支持。由于反映惯性测量组合退化状态的性能指标不能直接监测或直接测量带有噪声,因此需要构建状态空间模型预测惯性测量组合的剩余寿命。考虑到惯性测量组合的性能退化指标随时间呈现非线性特征,首先采用基于非线性漂移的Brown运动(Brownian motion, BM)建模其退化状态,然后基于构建的状态空间模型,利用期望最大化(expectation-maximization, EM)算法和Kalman滤波(Kalman filter)实时估计和更新退化状态和模型未知参数。并且将状态估计的分布函数引入剩余寿命的预测过程,近似得到了剩余寿命分布的解析形式,实现了剩余寿命的实时预测与更新。最后,对惯性测量组合的剩余寿命实时预测问题进行了实验分析,结果表明该方法具有较高的预测精度与较小的预测不确定性。
|
| 关键词 ：剩余寿命,预测,状态空间,非线性,期望最大化 |
Abstract：Real-time and accurate residual life prediction for an inertial measurement unit (IMU) can provide effective decision support for timely and cost-effective maintenance scheduling. The performance index reflecting the degradation of the IMU cannot be observed directly and direct measurements are contaminated by noise. Thus, a state space model was developed to predict the residual life of an IMU. Since the changes in the degradation state of the IMU are nonlinear over time, this analysis was a nonlinear drift-driven Brownian motion (BM) is used to characterize the degradation process, with the expectation maximization (EM) algorithm and the Kalman filter used to jointly estimate and update the state and model parameters. Furthermore, the estimated state distribution is incorporated into the residual life model using an approximate analytical form of the distribution. The approach is validated by comparison with experimental data which indicates that this method gives better prediction accuracies and lower uncertainties.
|
| Key words：residual lifepredictionstate spacenonlinearityexpectation maximization |
| 收稿日期: 2012-07-18 出版日期: 2015-04-17 |
| 基金资助:国家自然科学基金面上项目(61174030);国家青年科学基金项目(61004069) |
| [1] | Zhou Z J, Hu C H, Xu D L, et al.A model for real-time failure prognosis based on hidden Markov model and belief rule base [J]. European Journal of Operational Research, 2010, 207(1): 269-283. |
| [2] | Si X S, Wang W, Hu C H, et al.Remaining useful life estimation-A review on the statistical data driven approaches[J]. European Journal of Operational Research, 2011, 213(1): 1-14. |
| [3] | Sun J Z, Zuo H F, Pecht M G. Advances in sequential Monte Carlo methods for joint state and parameter estimation applied to prognostics [C]// Prognostics and System Health Management Conference. Shenzhen, China: IEEE Press, 2011: 1-7. |
| [4] | 冯磊, 王宏力, 韩晓霞等. 考虑监测噪声的陀螺仪剩余寿命在线预测[J]. 长春工业大学学报(自然科学版), 2013, 34(2):155-159. FENG lei, WANG Hongli, HAN Xiaoxia, et al. Real-time prediction on gyro residual life considering the monitoring noise[J]. Journal of Changchun University of Technology (Natural Science Edition), 2013, 34(2):155-159. (in Chinese) |
| [5] | Xu Z G, Ji Y D, Zhou D H. Real-time reliability prediction for a dynamic system based on the hidden degradation process identification[J]. IEEE Transactions on Reliability, 2008, 57(2): 230-242. |
| [6] | Christer A H, Wang W, Sharp J M. A state space condition monitoring model for furnace erosion prediction and replacement[J]. European Journal of Operational Research, 1997, 101(1): 1-14. |
| [7] | Batzel T D, Swanson D C. Prognostic health management of aircraft power generators[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2): 473-483. |
| [8] | Gasperin M, Juricic D, Boskoski P. Model-based prognostics of gear health using stochastic dynamical models[J]. Mechanical Systems and Signal Processing, 2010, 25(2): 537-548. |
| [9] | Cadini F, Zio E, Avram D. Monte Carlo-based filtering for fatigue crack growth estimation[J]. Probabilistic Engineering Mechanics, 2009, 24(3): 367-373. |
| [10] | Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful life of nonlinear components[J]. Reliability Engineering and System Safety, 2011, 96(3): 403-409. |
| [11] | Si X S, Wang W, Hu C H, et al.Remaining useful life estimation based on a nonlinear diffusion degradation process[J]. IEEE Transactions on Reliability, 2012, 61(1): 50-67. |
| [12] | Zhou Y F, Sun Y, Mathew J, et al.Latent degradation indicators estimation and prediction: A Monte Carlo approach[J]. Mechanical Systems and Signal Processing, 2011, 25(1): 222-236. |
| [13] | Lei F, Wang H L, Si X S, et al.A state space based prognostic model for hidden and age-dependent nonlinear degradation process[J]. IEEE Transactions on Automation Science and Engineering, 2013, 10(4): 1072-1086. |
| [14] | Lee M L T, Whitmore G A. Threshold regression for survival analysis: Modeling event times by a stochastic process reaching a boundary[J]. Statistical Science, 2006, 21(4): 501-513. |
| [15] | Ricciardi L M. On the transformation of diffusion processes into the Wiener process[J]. Journal of Mathematical Analysis and Applications, 1976, 54(1): 185-199. |
| [16] | 王志贤. 最优状态估计与系统辨识 [M]. 西安: 西北工业大学出版社, 2004. WANG Zhixian. Optimal State Estimation and System Identification [M]. Xi'an: Northwestern Polytechnical University Press, 2004. (in chinese) |
| [17] | Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society, Series B, 1977, 39(1): 1-38. |
| [18] | Schon T B. An explanation of the expectation maximization algorithm [Z]. Division of Automatic Control, Linkoping University, Linkoping, Sweden, Tech.Rep. LITH-ISY-R-2915, 2009. |
| [19] | 周志杰. 置信规则库在线建模方法与故障预测 [D]. 西安: 第二炮兵工程学院, 2010. ZHOU Zhijie. Online modeling methods of belief rule base and failure prognosis [D]. Xi'an: The Second Artillery Engineering University, 2010. (in chinese) |
| [20] | 胡昌华, 司小胜. 基于信度规则库的惯性平台健康状态参数在线估计[J]. 航空学报, 2010, 31(7): 1454-1465. HU Changhua, SI Xiaosheng. Real-time parameters estimation of belief rule base health condition for inertial platform[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(7): 1454-1465. (in chinese) |
| [21] | Carr M J, Wang W. An approximate algorithm for prognostic modelling using condition monitoring information[J]. European Journal of Operational Research, 2011, 211(1): 90-96. |
| [22] | Si X S, Hu C H, Wang W, et al.An adaptive and nonlinear drift-based Wiener process for remaining useful life estimation [C]//Prognostics and Health Management Conference, Shenzhen, China: IEEE Press, 2011: 1-5. |
| [23] | Carr M J, Wang W. Modeling failure modes for residual life prediction using stochastic filtering theory[J]. IEEE Transactions on Reliability, 2010, 59(2): 90-96. |
)
