Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling
Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling
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DOI：
10.1109/tr.2014.2299151
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发表时间：
2014-03-01
影响因子：
5.9
通讯作者：
Zhou, Dong-Hua
中科院分区：
文献类型：
--
作者：
Si, Xiao-Sheng;Wang, Wenbin;Zhou, Dong-Hua
使用观测到的系统退化数据可以帮助估计其剩余使用寿命（RUL）。然而，系统的退化进程通常是随机的，因此RUL也是随机变量，导致难以确定地估计RUL。一般来说，有三个来源的变异性有助于估计的RUL的不确定性：1）时间变异性，2）单位到单位的变异性，和3）测量变异性。在本文中，我们提出了一个相对一般的退化模型的基础上的维纳过程。在所提出的模型中，上述三个源的变异性的同时特征，将三个源的变异性的影响纳入RUL估计。通过建立状态空间模型，采用卡尔曼滤波技术估计相关的潜在退化状态和随机效应参数的后验分布。此外，不仅概率分布的分析形式，但也估计RUL的均值和方差的推导，并可以实时更新，符合新的退化观测的到来。我们还研究了模型参数估计中的可辨识性问题，并建立了相应的结果。为验证所提方法的有效性，以某惯性平台陀螺为例进行了仿真研究，结果表明，考虑三源变异性可以提高建模拟合度和RUL估计精度。
The use of the observed degradation data of a system can help to estimate its remaining useful life (RUL). However, the degradation progression of the system is typically stochastic, and thus the RUL is also a random variable, resulting in the difficulty to estimate the RUL with certainty. In general, there are three sources of variability contributing to the uncertainty of the estimated RUL: 1) temporal variability, 2) unit-to-unit variability, and 3) measurement variability. In this paper, we present a relatively general degradation model based on a Wiener process. In the presented model, the above three-source variability is simultaneously characterized to incorporate the effect of three-source variability into RUL estimation. By constructing a state-space model, the posterior distributions of the underlying degradation state and random effect parameter, which are correlated, are estimated by employing the Kalman filtering technique. Further, the analytical forms of not only the probability distribution but also the mean and variance of the estimated RUL are derived, and can be real-time updated in line with the arrivals of new degradation observations. We also investigate the issues regarding the identifiability problem in parameter estimation of the presented model, and establish the according results. For verifying the presented approach, a case study for gyros in an inertial platform is provided, and the results indicate that considering three-source variability can improve the modeling fitting and the accuracy of the estimated RUL.