摘要
为提高机械装备的可靠性与安全性,对轴承进行故障识别势在必行。然而当训练样本量缺乏时,现有故障识别模型的精度会大幅下降,同时轴承运行过程中的噪声干扰和负载变动,使其故障识别面临显著困难与挑战。针对上述问题本文提出了一种可信多尺度二次注意力卷积神经网络模型,该模型在充分考虑特征金字塔的思想上,首先采用适用于轴承振动信号的多尺度宽卷积核,其次在后续特征提取阶段采用小卷积核,并在此阶段引入了包含注意力机制的二次神经元,最后在多尺度特征融合阶段通过将模型的输出转化为狄利克雷分布,再利用DS证据理论进行融合,达到可信分类。实验结果表明该模型具有优异的泛化能力和鲁棒性,在各种样本缺乏时的复杂工况下,其故障识别性能均优于其他对比模型,表现出极具竞争力的故障识别结果。
To improve the reliability and safety of mechanical equipment,it is imperative to carry out fault identification on bearings.However,when the number of training samples is insufficient,the accuracy of existing fault identification models will drop significantly.At the same time,the noise interference and load variation during the operation of the bearing make its fault identification face significant difficulties and challenges.Aiming at the above problems,a credible multi-scale quadratic attention convolutional neural network model was proposed,which adopted a multi-scale wide convolution kernel suitable for bearing vibration signals by fully considering the idea of the feature pyramid firstly.Then,a small convolution kernel was used in the subsequent feature extraction stage,and quadratic neuron including attention mechanism was introduced at this stage.In the multi-scale feature fusion stage,the outputs of the model were transformed into a Dirichlet distribution,and then fused using DS evidence theory to achieve credible classification.The experimental results showed that the model had excellent generalization ability and robustness,and its fault identification performance was superior to other comparison models under various complex operating conditions when samples were scarce,showing highly competitive fault identification results.
作者
唐宇恒
张超勇
张道德
吴剑钊
薛敬宇
TANG Yuheng;ZHANG Chaoyong;ZHANG Daode;WU Jianzhao;XUE Jingyu(School of Mechanical Science&Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China;Wuhan Heavy Duty Machine Tool Group Corporation,Wuhan 430205,China)
出处
《计算机集成制造系统》
北大核心
2025年第8期3021-3032,共12页
Computer Integrated Manufacturing Systems
基金
国家重点研发计划政府间国际合作专项资助项目(2022YFE0114200)
国家自然科学基金资助项目(52205527)。
关键词
轴承故障识别
二次注意力卷积
多尺度学习
可信分类
bearing fault identification
quadratic attention convolution
multi-scale learning
credible classification
作者简介
唐宇恒(1999-),男,江西丰城人,硕士研究生,研究方向:风险与可靠性、装备故障诊断、深度学习等,E-mail:yhtang@hust.edu.cn;通讯作者:张超勇(1972-),男,江苏海门人,教授,博士,研究方向:智能调度算法、网络化制造、绿色制造等,E-mail:zcyhust@hust.edu.cn;张道德(1973-),男,湖北黄梅人,教授,博士,研究方向:机器视觉检测与故障诊断等;吴剑钊(1992-),男,福建泉州人,博士研究生,研究方向:激光低碳制造、制造系统优化;薛敬宇(1980-),男,内蒙古通辽人,正高级工程师,硕士,研究方向:数控机床设计。
