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基于双应力加速寿命试验的关节轴承寿命预测与可靠性分析 被引量:1

Life Prediction and Reliability Analysis of Spherical Plain Bearings Based on Double Stress Accelerated Life Test
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摘要 自润滑关节轴承寿命主要是由衬垫的磨损性能决定的,但衬垫的磨损一般是非线性的,使得寿命难以预测。采用同时改变载荷和摆频2种应力的方法进行加速寿命试验,建立以pv值、磨损量退化数据为输入参数,寿命值为输出参数的灰色神经网络预测模型。经验证该预测模型对关节轴承寿命预测的最大误差仅为7.33%,平均误差仅为3.892%。对不同加速应力下自润滑关节轴承可靠性进行评估,结果表明,关节轴承的可靠性在L 10(可靠度为90%时的寿命)之前下降趋势缓慢,然后迅速下降,pv值越大可靠性下降越迅速;随着pv值的增大,关节轴承寿命近似呈指数下降,经验证可用逆幂率加速模型反映二者关系。 The life of woven liner self-lubricating spherical plain bearing is mainly determined by the wear performance of the liner,which is generally non-linear,making it difficult to predict the life.Based on the method of simultaneous change of load and swing frequency for accelerated life test,a gray neural network prediction model was established with pv value,wear amount degradation data as input parameters and life value as output parameters.It was verified that the maximum error predicted by the prediction model is only 7.33%,and the average error is only 3.892%.The reliability of self-lubricating spherical plain bearing under different acceleration stresses was evaluated.It is concluded that the reliability of the spherical plain bearing is decreased slowly before L 10(lifetime at 90%reliability),and then decreased rapidly.The greater the pv value,the faster the reliability decreases.As the pv value increases,the life of the spherical plain bearing is decreased exponentially,the inverse power law model can be used to reflect the relationship between the model responses.
作者 张亚涛 邱明 周大威 卢团良 庞晓旭 ZHANG Yatao;QIU Ming;ZHOU Dawei;LU Tuanliang;PANG Xiaoxu(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang Henan 471003,China;Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province,Luoyang Henan 471003,China)
出处 《润滑与密封》 CAS CSCD 北大核心 2020年第3期51-56,共6页 Lubrication Engineering
基金 国家自然科学基金项目(51275155) 河南省科技创新杰出人才计划项目(154200510013) 河南省重大科技专项(161100210800).
关键词 自润滑关节轴承 双应力 灰色神经网络 寿命预测 可靠性 self-lubricating spherical plain bearing double stress gray neural network life prediction reliability
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