基于Box-Cox变换与随机系数回归模型的非线性退化设备剩余寿命预测方法

doi:10.7527/S1000-6893.2022.27660

Abstract

Abstract:

Accurate prediction of Remaining Useful Life （RUL） of degraded equipment can provide important information support for equipment maintenance management， thereby avoiding unplanned failure and reducing the operating cost of equipment. Aiming at the nonlinear degradation phenomenon widely existing in practical engineering， this paper proposes a RUL prediction method of nonlinear equipment based on the Box-Cox transformation and random coefficient regression model. The Box-Cox transformation is used to linearize the nonlinear degradation data， the degradation model is then constructed through the random coefficient regression model based on the transformed degradation data， and the model parameters are updated online by the Bayesian theory and Monte Carlo expected maximization algorithm. Based on the characteristics of the random coefficient regression model， the distribution function of RUL and its point estimation value are derived. Finally， the effectiveness of the proposed method is verified by numerical simulation and actual degradation data of a lithium battery.

Key words: remaining useful life (RUL), Box-Cox transformation, random coefficient regression model, nonlinear degradation data, Monte Carlo expected maximization algorithm

CLC Number:

V239

Baokui YANG, Jianxun ZHANG, Huiqin LI, Xiaosheng SI. Remaining useful life prediction method for nonlinear degrading equipment based on Box-Cox transformation and random coefficient regression model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(11): 227660-227660.

Figures/Tables 13

Fig. 1

Table 1

Parameter estimation results obtained by data accumulation

组数	$μ φ$	$μ θ$	$σ 2$
真实值	0.020 0	0.005 0	0.000 1
3	0.008 6	0.017 7	0.003 4
7	0.030 8	0.007 1	0.003 1
10	0.012 1	0.007 1	0.000 3
15	0.020 6	0.005 6	0.000 1
18	0.020 1	0.005 6	0.000 1
21	0.020 4	0.005 2	0.000 1

Table 1

Fig. 2

Fig. 3

Fig. 4

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Table 2

Comparison of model prediction performance

数据类别		Score	MSE	$R 2$	$r ·, t$	$δ$ /%
仿真退化数据	BCT前	8.059 7	3.318 4	0.885 6	0.900 9	11.46
仿真退化数据	BCT后	2.310 3	0.001 4	0.952 1	0.991 4	3.35
锂电池退化数据	BCT前	1.847 0	0.913 9	0.838 4	-0.890 7	22.17
锂电池退化数据	BCT后	1.186 5	0.045 1	0.967 6	-0.970 7	12.96

Table 2

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[1]	Tianmei LI, Xiaosheng SI, Jianxun ZHANG. Data-model interactive remaining useful life prediction method for multi-sensor monitored linear stochastic degrading devices [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(8): 227190-227190.
[2]	Chao REN, Huiqin LI, Tianmei LI, Jianxun ZHANG, Xiaosheng SI. Equipment remaining useful life prediction method with dynamic calibration of degradation model [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(19): 228345-228345.

Remaining useful life prediction method for nonlinear degrading equipment based on Box-Cox transformation and random coefficient regression model

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