考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计

Remaining Useful Life Estimation for Nonlinear Stochastic Degrading Systems with Uncertain Measurement and Unit-to-unit Variability

剩余寿命估计是预测与健康管理的基础,是降低系统运行风险、提高系统安全性与可靠性的有效途径.针对工程实际中大量存在的非线性随机性退化系统,现有方法仅单独考虑了不确定测量或系统间个体差异对剩余寿命的影响,尚未实现同时考虑不确定测量和个体差异的剩余寿命估计.因此,本文首先建立了一种基于扩散过程的非线性退化模型,进一步通过建立的状态空间模型和Kalman滤波实现了同时考虑不确定测量和个体差异下的随机退化系统剩余寿命自适应估计,同时对漂移系数进行自适应估计,以获取非线性退化系统更加精确的剩余寿命估计.最后,将所提方法应用于疲劳裂纹和陀螺仪的监测数据,结果表明本文方法显著优于仅考虑不确定测量或仅考虑个体差异的寿命估计方法,具有潜在的工程应用价值.

Remaining useful life （RUL） estimation is essential for the prognostics and health management of systems, and is the effective path to mitigate system risk and improve the system safety and reliability. For the extensively encountered practical degradation systems with nonlinearity and stochasticity, the current methods to estimate RUL only consider uncertain measurement or unit-to-unit variability, but not both simultaneously. In this paper, a nonlinear degradation model is built based on a nonlinear diffusion degradation process to incorporate the uncertain measurement and unit-to- unit variability into the estimated RUL. By constructing a state-space model and applying Kalman filtering technique, an analytical form of the RUL distribution is derived. In addition, the RUL estimation and drift coefficient efficient can be adaptively updated with the available observations. Finally, two cases study for aluminium alloy in aircraft and gyros are provided to verify the presented method. The results illustrate that the presented method can generate better results than only considering uncertain measurement or unit-to-unit variability, and thus can be potentially applied in practice.