基于VMD-EEMD-LSTM的涂层型关节轴承剩余使用寿命预测方法

doi:10.3901/JME.2023.09.125

机械工程学报 ›› 2023, Vol. 59 ›› Issue (9): 125-136.doi: 10.3901/JME.2023.09.125

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基于VMD-EEMD-LSTM的涂层型关节轴承剩余使用寿命预测方法

林亮行¹, 马国政², 孙建芳¹, 韩翠红^2,3, 雍青松⁴, 苏峰华¹, 王海斗^2,3

1. 华南理工大学机械与汽车工程学院广州 510000;
2. 陆军装甲兵学院机械产品再制造国家工程研究中心北京 100071;
3. 陆军装甲兵学院装备再制造技术国防科技重点实验室北京 100071;
4. 中国空气动力研究与发展中心设备设计与测试技术研究所绵阳 621000

收稿日期:2022-05-01 修回日期:2022-10-19 出版日期:2023-05-05 发布日期:2023-07-19
通讯作者: 马国政(通信作者),男,1984 年出生,博士,副研究员。主要研究方向为表面工程、再制造工程、摩擦学。E-mail:magz0929@163.com E-mail:magz0929@163.com
作者简介:林亮行,男,1998年出生。主要研究方向为机械零部件寿命预测。E-mail:963931707@qq.com
基金资助:
国家自然科学基金(52122508，52105200，51905533)和“十四五”预研资助项目。

Remaining Useful Life Prediction Method of Coated Spherical PlainBearing Based on VMD-EEMD-LSTM

LIN Liangxing¹, MA Guozheng², SUN Jianfang¹, HAN Cuihong^2,3, YONG Qingsong⁴, SU Fenghua¹, WANG Haidou^2,3

1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510000;
2. National Engineering Research Center for Remanufacturing, PLA Armored Force Academy, Beijing 100071;
3. National Key Laboratory for Remanufacturing, PLA Armored Force Academy, Beijing 100071;
4. Facility Design and Instrumentation Institute of China Aerodynamics Research and Development Center, Mianyang 621000

Received:2022-05-01 Revised:2022-10-19 Online:2023-05-05 Published:2023-07-19

摘要/Abstract

摘要： 涂层型关节轴承由于其结构紧凑且具有较好的摩擦性能,在航天航空设备领域有广泛的应用前景,对其剩余使用寿命(Remaining useful life,RUL)进行有效预测,能够为设备的维护提供一定的理论依据。因此,提出了一种基于变分模态分解(Variational mode decomposition,VMD)、集成经验模态分解(Ensemble empirical mode decomposition,EEMD)以及长短期记忆神经网络(Long Short-term memory neural network,LSTM)模型的剩余使用寿命预测方法。首先,利用VMD以及EEMD对轴承摩擦扭矩信号进行特征提取,并根据时间相关性进行特征筛选,得到相关性较高的3组特征序列,对筛选出的特征进行相对归一化处理作为模型输入,减小不同工况下摩擦扭矩幅值变化带来的影响;最后,选择超参数优化区间对LSTM进行贝叶斯优化,得到贝叶斯优化-LSTM模型,对涂层型关节轴承的RUL进行预测。研究结果表明,该预测模型融入了能够表征涂层型关节轴承退化信息和寿命衰减的多个信号特征,对不同工作载荷下的轴承均有较高的RUL预测精度以及较好的泛化性能。

关键词: 涂层型关节轴承, 变分模态分解, 集成经验模态分解, 长短期记忆神经网络, 贝叶斯优化

Abstract: Because of its compact structure and good friction performance, coated spherical plain bearing has a wide application prospect in the field of aerospace equipment. The effective prediction of its remaining useful life (RUL) can provide a certain theoretical basis for equipment maintenance. Therefore, a prediction method of RUL based on Long Short-term memory neural network (LSTM), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) is proposed. First of all, VMD and EEMD are used to extract the features of bearing friction torque signal. Features were selected according to Pearson correlation coefficient between features and bearing swing times and 3 groups of feature sequences with high correlation coefficients were selected. The selected features are relatively normalized as the model input to reduce the influence of friction torque amplitude changes under different working conditions. Finally, the hyperparameter optimization interval is selected to perform Bayesian optimization on LSTM, so as to obtain the Bayesian optimization-LSTM model and this model is constructed to predict the RUL of coated spherical bearing. The results show that proposed the model integrates multiple signal features that can characterize the degradation information of coated spherical bearings, and has high prediction accuracy of RUL for bearings under different working conditions, and also shows its good generalization performance.

Key words: coated spherical plain bearing, variational mode decomposition, ensemble empirical mode decomposition, long short-term memory neural network, Bayes optimization

中图分类号:

TG156

林亮行, 马国政, 孙建芳, 韩翠红, 雍青松, 苏峰华, 王海斗. 基于VMD-EEMD-LSTM的涂层型关节轴承剩余使用寿命预测方法[J]. 机械工程学报, 2023, 59(9): 125-136.

LIN Liangxing, MA Guozheng, SUN Jianfang, HAN Cuihong, YONG Qingsong, SU Fenghua, WANG Haidou. Remaining Useful Life Prediction Method of Coated Spherical PlainBearing Based on VMD-EEMD-LSTM[J]. Journal of Mechanical Engineering, 2023, 59(9): 125-136.

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基于VMD-EEMD-LSTM的涂层型关节轴承剩余使用寿命预测方法

Remaining Useful Life Prediction Method of Coated Spherical PlainBearing Based on VMD-EEMD-LSTM

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