中国机械工程2024，Vol.35Issue(8)：1405-1413,1448,10.DOI:10.3969/j.issn.1004-132X.2024.08.009
基于时频融合深度网络的矿用钻机轴承故障诊断
Bearing Fault Diagnosis of Mining Drilling Rig with Time-frequency-fused Deep Network
摘要
Abstract
To solve the problems of weak and noisy bearing fault features caused by the low-speed and heavy-load operating characteristics of mining drilling rigs,a fault diagnosis method was proposed for mining rig bearings,named time-frequency-fused deep network.It considered the limitations of fault diagnosis with single modality,and then jointly characterizes two modal features of the time do-main and time-frequency domain.The designed diagnostic network differentially embeded specific at-tention mechanism in different modules to extract multi-dimensional key fault features.Finally,the proposed method was validated on the experimental equipment and the Case Western Reserve Univer-sity bearing dataset.The results show that the proposed method may automatically extract sufficient fault features combining two domains.It has higher accuracy and noise immunity than those with a single domain.关键词
矿用钻机轴承/故障诊断/时频融合/注意力机制/空洞卷积Key words
bearings of mining drilling rig/fault diagnosis/time-frequency fusion/attention mechanism/dilated convolution分类
矿业与冶金引用本文复制引用
邹筱瑜,孙国庆,王忠宾,潘杰,刘新华,李鑫..基于时频融合深度网络的矿用钻机轴承故障诊断[J].中国机械工程,2024,35(8):1405-1413,1448,10.基金项目
国家自然科学基金(62273349,62176258) （62273349,62176258）
国家重点研发计划(2020YFB1314200) （2020YFB1314200）
中央高校基本科研业务费(2021YCPY0111) （2021YCPY0111）
江苏省高校优势学科建设工程(PAPD) （PAPD）
