基于核函数-Wiener过程的轧辊非线性退化建模与剩余寿命预测

doi:10.16183/j.cnki.jsjtu.2022.004

摘要/Abstract

摘要：

在轧钢生产过程中,由于磨损等原因,长时间复杂工况运行下的轧辊工作性能会呈现逐步衰退的特征.考虑轧辊工作环境具有工况复杂、随机干扰强等特点,提出基于核函数-Wiener过程(KWP)退化模型,使用Wiener过程刻画轧辊退化趋势的强随机性特征,引入核函数捕捉轧辊的非线性退化路径,推导贝叶斯框架下参数估计的解析表达式,并构造轧辊可工作转动量的健康指标,进一步预测轧辊剩余寿命(RUL).结合某钢铁公司1580热轧生产线现场数据,所构建模型拟合优度达到0.989,剩余寿命预测误差低于4.7%,相较常见机器学习算法取得了更好效果,有助于提高设备运转效率与安全性并实现视情维护.

关键词: 轧辊, 性能退化, 非线性建模, 核函数, Wiener过程, 剩余寿命预测

Abstract:

In the process of steel rolling, due to wear and other reasons, the working performance of the roll under long and complex working conditions has a gradual decline. Considering the characteristics of complex working conditions and strong random interference of the roll working environment, this paper proposed a kernel-based Wiener process (KWP) degradation model to characterize the strong randomness of the roll degradation trend by using the Wiener process, and to capture the nonlinear degradation path of the roll by using the kerna function. This paper derives the analytical expression of parameter estimation in the Bayesian framework, and constructs the health index of the roll working rotation, then predicts the remaining useful life (RUL) of the roll. In combination with the field data of 1580 hot rolling production line of an iron and steel company, the goodness of fit of the model built is 0.989, and the residual life prediction error is less than 4.7%. Compared with the common machine learning algorithm, it has achieved better results, which is helpful to improve the operating efficiency and safety of equipment and achieve maintenance as needed.

Key words: roller, performance degradation, nonlinear modeling, kernel function, Wiener process, remaining useful life (RUL) prediction

中图分类号:

TH17

王汉禹, 陈震, 周笛, 陈兆祥, 潘尔顺. 基于核函数-Wiener过程的轧辊非线性退化建模与剩余寿命预测[J]. 上海交通大学学报, 2023, 57(8): 1037-1045.

WANG Hanyu, CHEN Zhen, ZHOU Di, CHEN Zhaoxiang, PAN Ershun. Nonlinear Degradation Modeling and Residual Life Prediction for Rollers Based on Kernel-based Wiener Process[J]. Journal of Shanghai Jiao Tong University, 2023, 57(8): 1037-1045.

图/表 10

表1

常用核函数及其公式

核函数	表达式	参数
线性核函数	$t t i$	无特殊参数
高斯核函数	$e x p - t - t i \| 2 2 v 2$	v为高斯核函数的宽度
多项式核函数	$(t t i + r) d, ? d = 1,2, …, n$	$r 为偏置系数; d$ 为多项式阶数
Laplace核函数	$e x p - t - t i \| o$	o>0
Sigmoid核函数	$t a n h (ρ t t i + θ)$	$ρ > 0, θ < 0$

表1

图1

图2

图3

表2

表3

图4

表4

表5

图5

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轧辊编号	上机时间	下机时间	生产量/t	轧制距离/km	轧辊直径/mm
1580I1	2017-12-27T15:04	2017-12-27T17:36	1016	4.863	825.92
1580I2	2017-12-27T20:27	2017-12-27T22:42	739	4.803	825.92
1580I2	2017-12-28T1:04	2017-12-28T4:34	1427	6.588	825.83
1580I2	2017-12-28T12:26	2017-12-28T17:48	2570	14.504	825.83
1580I2	2017-12-29T10:32	2017-12-29T17:25	2360	14.013	825. 83

核函数类型	RMSE	CD	MAE
高斯核函数	1109.8	0.9893	865.1
多项式核函数	7713.1	0.4781	5916.9
线性核函数	10717.1	0.0000	7589.3
Laplace核函数	2046.5	0.8664	1475.4
Sigmoid核函数	10717.0	0.0000	7589.3

模型	RMSE	CD	MAE
基于高斯核函数的Wiener过程模型	1109.7	0.9893	865.1
指数Wiener过程模型	8059.8	0.4726	6369.4
基于高斯核函数的相关向量机模型	1992.7	0.8719	1591.2

时间序列	RUL预测值	RUL实际值	误差/%
No.80	53790.5	56537.7	4.9
No.100	52630.2	52021.6	1.2
No.120	40764.1	37671.3	8.2