Remaining Useful Life Prediction of Multi-Degradation Feature Equipment Considering Time-Varying Correlation

ZHANG Jialing; ZHANG Jianxun; DU Dangbo; ZHANG Zhengxin; HU Changhua

doi:10.7652/xjtuxb202510020

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Remaining Useful Life Prediction of Multi-Degradation Feature Equipment Considering Time-Varying Correlation

更新时间：2025-09-28

- Remaining Useful Life Prediction of Multi-Degradation Feature Equipment Considering Time-Varying Correlation
- Journal of Xi'an Jiaotong University Vol. 59, Issue 10, Pages: 210-220(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202510020
  CLC： TH17
- Received：10 April 2025，
  
  Published：10 October 2025
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ZHANG Jialing, ZHANG Jianxun, DU Dangbo, et al. Remaining Useful Life Prediction of Multi-Degradation Feature Equipment Considering Time-Varying Correlation[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 210-220.
DOI：

ZHANG Jialing, ZHANG Jianxun, DU Dangbo, et al. Remaining Useful Life Prediction of Multi-Degradation Feature Equipment Considering Time-Varying Correlation[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 210-220. DOI： 10.7652/xjtuxb202510020.

摘要

针对多退化特征设备剩余寿命预测中现有方法采用静态相关性模型，对相关性时变形式做潜在假定导致模型在复杂工况下适应性不足的问题，提出一种基于时变Copula函数的多退化特征设备剩余寿命预测方法。基于非线性维纳过程建立各自退化特征模型，利用时变Copula函数构建多个特征退化模型间的时变相关性；通过贝叶斯马尔可夫链蒙特卡罗梅特罗波利斯-黑斯廷斯算法与极大似然估计算法，实现非线性维纳过程模型和时变Copula函数的参数辨识；依据赤池信息量准则优选时变Copula函数形式，结合边缘分布推导设备预测的剩余寿命联合分布。通过数值仿真数据和高炉炉壁的实际数据进行验证，结果表明：所提方法得到的剩余寿命平均均方误差相比3个退化特征独立情况降低了8.62%，比静态Copula函数降低了5.57%，比动态贝叶斯网络减少了17.85%；所提方法能够有效表征多退化特征的时变退化相关性对于剩余寿命预测的影响。该方法可为多退化特征设备剩余寿命预测提供一种有效解决方案。

Abstract

To address the issue of insufficient adaptability of existing methods in remaining useful life (RUL) prediction for multi-degradation feature equipment

where static correlation models or potential assumptions about time-varying correlation forms lead to poor performance under complex operating conditions

a time-varying Copula-based RUL prediction method is proposed. First

nonlinear Wiener processes are employed to establish models for individual degradation features

while time-varying Copula functions are introduced to capture dynamic correlations among multiple degradation models. Second

parameter identification for both the nonlinear Wiener processes and the time-varying Copula functions is achieved via Bayesian Markov chain Monte Carlo Metropolis-Hastings and maximum likelihood estimation. Finally

the optimal time-varying Copula form is selected using the Akaike information criterion

and the joint distribution of predicted RUL is derived by integrating marginal distributions. Verification is performed using numerical simulation data and actual data from a blast furnace wall. Experimental results indicate that the proposed method reduces the mean square error of RUL predictions by 8.62% compared to independent modeling of three degradation characteristics

5.57% over static Copula function

and 17.85% against dynamic Bayesian network methods. The proposed method can accurately characterizes the impact of time-varying degradation correlations of multiple degenerative features on RUL prediction

offering a more effective solution for predicting the remaining useful life of multi-degradation feature equipment.

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references

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