摘要
滚动轴承作为机床主轴的关键部件,其剩余寿命预测直接决定着整机设备的剩余寿命。若不能及时地预知滚动轴承的健康状态或损伤情况,不仅会影响维修策略的制定,还会造成级联故障,易造成机床灾难性的事故。针对大数据下滚动轴承振动信号的自适应故障特征提取和智能诊断问题,构建卷积神经网络和长短期记忆网络(CNN-LSTM)相结合的寿命预测模型,它可以避免人工参与的影响,实现网络的互补优势。对滚动轴承的退化状态以及剩余寿命进行预测,并与卷积神经网络(CNN)、长短时记忆神经网络(LSTM)进行对比实验。结果表明:所提方法CNN-LSTM有着较高的预测准确度。
As a key component of machine tool spindle,the remaining useful life prediction of rolling bearings directly determines the remaining life of the whole mechanical equipment.If the health status or damage of rolling bearings cannot be predicted in time,it will not only affect the formulation of maintenance strategies,but also cause cascading failures,which is likely to cause catastrophic accidents of mechanical equipment.Aiming at the problem of adaptive fault feature extraction and intelligent diagnosis of rolling bearing vibration signals under big data,a life prediction model combining convolutional neural network and long short-term memory network(CNN-LSTM)was constructed,which could avoid the influence of manual participation and realize the complementary advantages of the network.The degradation state and residual life of rolling bearings were predicted,and compared with convolutional neural network(CNN)and long short-term memory neural network(LSTM).The experimental results show that CNN-LSTM has higher prediction accuracy.
作者
姜广君
杨金森
穆东明
JIANG Guangjun;YANG Jinsen;MU Dongming(School of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot Inner Mongolia 010051,China;Inner Mongolia Key Laboratory of Advanced Manufacturing Technology,Hohhot Inner Mongolia 010051,China)
出处
《机床与液压》
北大核心
2024年第6期184-189,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金地区科学基金项目(51965051
71761030)
内蒙古自治区关键技术攻关计划(2021GG0346)
内蒙古自治区关键技术攻关计划(2019LH07003)。
关键词
卷积神经网络
长短时神经网络
剩余寿命
滚动轴承
convolutional neural network
long short-term memory neural network
remaining useful life
rolling bearings
