基于多源信息的隐含非线性维纳退化过程剩余寿命预测

doi:10.7527/S1000-6893.2022.27662

Abstract

Abstract:

Accurate remaining useful life prediction is of huge significance in improving the reliability and safety of the system. Practical engineering often encounters imperfect or scarce prior degradation information， with the nonlinear degradation characteristics difficult to track by the linear degradation models. To solve this problem， we propose a remaining useful life prediction method for nonlinear degradation systems with measurement errors by reasonably fusing failure time data and multi-source information， based on the implicit nonlinear Wiener degradation process model. Firstly， we obtain the relationship between the degradation data and the nature of parameter estimation based on the implicit nonlinear Wiener degradation process by theoretical derivation， providing a theoretical basis for reasonable fusion of multi-source information. Secondly， according to the nature of parameter estimation， we use the field degradation data and historical degradation data to estimate the model fixed parameters of the two prediction cases respectively and fuse the failure time data into the degradation model by the Expectation Maximization （EM） algorithm. Then， the Kalman filtering algorithm is used to update online the drift parameter based on the field degradation data. Finally， simulation experiments are conducted to further verify the nature of parameter estimation， and two practical case studies are used to verify the superiority of the proposed method.

Key words: remaining useful life prediction, measurement error, implicit nonlinear Wiener process, multi-source information, Kalman filtering

CLC Number:

Jiaxin YANG, Shengjin TANG, Liang LI, Xiaoyan SUN, Shuai QI, Xiaosheng SI. Remaining useful life prediction of implicit nonlinear Wiener degradation process based on multi-source information[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(12): 227662-227662.

Figures/Tables 23

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Table 1

Prior parameters of M0 at different times

循环次数/10⁴	3	4	5	6	7	8	9
$μ λ$	7.275 4	7.388 4	6.743 9	6.991 0	7.287 8	8.631 9	10.147 6
$σ λ 2$	1.511 0	1.578 9	1.215 1	1.347 8	1.518 3	2.450 6	3.839 9
$σ B 2$	$4.7 × 10 - 16$	$8.5 × 10 - 13$	$6.3 × 10 - 16$	$1.8 × 10 - 17$	$7.8 × 10 - 15$	$7.7 × 10 - 14$	$1.7 × 10 - 16$
$b$	1.105 6	1.112 8	1.070 0	1.086 9	1.106 4	1.185 9	1.261 7
$σ ε 2$	$5.9 × 10 - 5$	$4.4 × 10 - 5$	$3.8 × 10 - 5$	$3.2 × 10 - 5$	$2.9 × 10 - 5$	$5.7 × 10 - 9$	$8.7 × 10 - 5$

Table 1

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Remaining useful life prediction of implicit nonlinear Wiener degradation process based on multi-source information

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