Remaining useful life prediction based on multi source information with considering random effects
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摘要:
为了合理利用同类设备的先验信息，提高参数估计和剩余使用寿命（RUL）预测精度，提出一种基于多源信息融合并考虑随机效应的RUL预测方法。利用考虑随机效应的线性Wiener过程对设备的退化过程进行建模；利用期望最大化（EM）算法，融合先验退化信息和先验失效寿命数据信息，计算模型中的未知参数；根据Wiener过程参数估计的性质，提出一种基于多源信息融合并考虑随机效应的非线性Wiener过程参数估计方法；利用激光器数据和疲劳裂纹数据进行实验验证。实验结果表明：与基于历史退化数据或失效寿命数据的方法相比，所提方法能有效提高参数估计和RUL预测的精度。
Abstract:In order to reasonably utilize the prior information of congeneric equipment and improve the accuracy of parameters estimation and remaining useful life (RUL) prediction, a RUL prediction method based on multi source information considering the random effects is proposed. A linear Wiener process considering the random effects was employed to model the degradation process of equipment. The expectation maximization (EM) algorithm was used to calculate unknown parameters in model with fusing prior degradation information and prior failure time data information. According to the nature of parameter estimation based on the Wiener process, a method based on multi source information for nonlinear Wiener process considering random effects was proposed. Laser data and fatigue crack data were used for experimental verification. The results show that compared with the method based on historical degradation data or failure time data, the proposed method can effectively improve the accuracy of parameters estimation and RUL estimation.
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Key words:
- multi source information fusion /
- random effects /
- Wiener process /
- remaining useful life /
- nonlinear
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图 4 ${\mu _\lambda }$随样本数量$N$变化的参数估计结果
Figure 4. Estimated ${\mu _\lambda }$ with change of sample number $N$
图 5 $\sigma _\lambda ^2$随样本数量$N$变化的参数估计结果
Figure 5. Estimated $\sigma _\lambda ^2$ with change of sample number $N$
图 6 $\sigma _B^2$随样本数量$N$变化的参数估计结果
Figure 6. Estimated $\sigma _B^2$ with change of sample number $N$
图 7 非线性Wiener过程的仿真退化数据
Figure 7. Simulation degradation data of nonlinear Wiener process
图 8 $\theta $随样本数量$N$和检测时间变化$k$的参数估计结果
Figure 8. Estimated $\theta $ with change of sample number $N$ and detection time $k$
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