摘要
滚动轴承大量使用在旋转机械中,轴承的工况严重影响着机械设备的正常运行。为了提高轴承故障的诊断精度,本文提出了一种时频分析和深度学习相结合的滚动轴承诊断方法。首先,分析了十种不同时频分析方法;其次,建立了深度学习的滚动轴承故障诊断模型,并利用迁移学习克服训练样本数量少的问题,通过对比分析,常数Q变换(Constant Q transform,CQT)的准确率可达100%;最后,利用实验数据对所提方法的有效性和可靠性进行验证,分别评估了在不同负载和噪声情况下的识别精度,并与文献中的方法对比,证明所提方法在不同工作环境条件下都有较好的鲁棒性和较高的识别精度。
Rolling bearings are widely used in rotating machinery,and the working conditions of the bearings seriously affect the normal operation of mechanical equipment.In order to improve the accuracy of bearing fault diagnosis,a new fault diagnosis method of rolling bearing combining time-frequency analysis with deep learning is proposed in this paper.Firstly,ten different time-frequency analysis methods are analyzed and compared.Then,the fault diagnosis model for rolling bearings using deep learning is established,and the transfer learning is applied to overcome the problem led by small number of training samples.By contrast,the accuracy of constant Q transform(CQT)can reach 100%.Finally,the effectiveness and reliability of the proposed method are verified via the experimental data.The recognition accuracies under different working loads and noise environment are evaluated respectively,and are compared to the results obtained by other methods in references.The results show that the proposed method has better robustness and higher recognition accuracy under different working environment conditions.
作者
任胜杰
郭伟超
舒定真
汤奥斐
高新勤
李言
REN Shengjie;GUO Weichao;SHU Dingzhen;TANG Aofei;GAO Xinqin;LI Yan(School of Mechanical and Precision Instrument Engineering,Xi′an University of Technology,Xi′an 710048,China)
出处
《机械科学与技术》
CSCD
北大核心
2023年第1期149-158,共10页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51505377,51575443)
陕西留学人员科技活动择优项目(302/253081605)
陕西省教育厅协同创新中心项目(20JY047)。
关键词
滚动轴承
故障诊断
时频分析
深度学习
迁移学习
rolling bearing
fault diagnosis
time-frequency analysis
deep learning
transfer learning
