摘要
为探索适用于涂层型自润滑关节轴承的寿命预测和可靠性评估方法,提出一种基于卷积神经网络(CNN)和长短期记忆(LSTM)神经网络的轴承剩余寿命预测模型。首先利用CNN对关节轴承的摩擦扭矩信号进行失效特征提取,然后将通过主成分分析(PCA)和滤波处理后的扭矩信号输入LSTM神经网络中进行训练,得到涂层型自润滑关节轴承寿命预测模型,可实现对轴承剩余寿命的准确预测。最后,基于加速寿命试验数据,采用两参数Weibull分布模型对涂层型自润滑关节轴承的服役可靠性进行评估,结果表明涂层型自润滑关节轴承在轻载低频工况下能够维持在高可靠性水平(90%)下进行长时间稳定服役。
A method for predicting the residual life of coated self-lubricating spherical bearings based on convolutional neural network(CNN)and long-short term memory neural network(LSTM)was proposed.Firstly,the failure features of the friction torque signal of the spherical bearing was extracted by CNN.Then the torque signals processed by principal component analysis(PCA)and filtering were input into LSTM neural network for training to obtain the life prediction model of coated self-lubricating spherical bearings,which enabled accurate predictions of the bearing residual life.Finally,based on the accelerated life tests,the reliability of coated selflubricating spherical bearings was evaluated using a two-parameter Weibull distribution model.The results indicate that coated self-lubricating spherical bearings can maintain long-term stable work at high reliability levels(90%)under light load and low frequency.
作者
刘云帆
林亮行
马国政
孙建芳
苏峰华
郭伟玲
朱丽娜
王海斗
LIU Yunfan;LIN Liangxing;MA Guozheng;SUN Jianfang;SU Fenghua;GUO Weiling;ZHU Lina;WANG Haidou(School of Engineering and Technology,China University of Geosciences(Beijing),Beijing 100083,China;National Key Laboratory for Remanufacturing,Army Academy of Armored Forces,Beijing 100072,China;School of Mechanical Engineering,South China University of Technology,Guangzhou 510000,China;National Engineering Research Center for Remanufacturing,Army Academy of Armored Forces,Beijing 100072,China)
出处
《航天器环境工程》
CSCD
北大核心
2023年第5期531-540,共10页
Spacecraft Environment Engineering
基金
国家自然科学基金项目(编号:52122508,52005511,52130509)。
关键词
涂层型自润滑关节轴承
卷积神经网络
长短期记忆神经网络
加速寿命试验
可靠性评估
coated self-lubricating spherical bearing
convolutional neural network
long-short term memory neural network
accelerated life test
reliability evaluation
