计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230700167-6.doi: 10.11896/jsjkx.230700167
张兰昕, 向玲, 李显泽, 陈锦鹏
ZHANG Lanxin, XIANG Ling, LI Xianze, CHEN Jinpeng
摘要： 滚动轴承是机械设备的关键部件,为了对滚动轴承的故障类别进行有效识别,提出了一种融合自注意力机制(Self-Attention,SA)和轻量级网络MobileNetV3的SAMNV3滚动轴承智能故障诊断模型。利用该模型中自注意力机制对特征进行自适应加权的优点以及轻量级网络MobileNetV3体积较小的优点,通过直接将两个不同数据集的原始振动信号输入SAMNV3模型中,进行故障的特征提取与识别分类,从而实现端到端的滚动轴承智能故障诊断。在两种不同的数据集上进行验证,结果表明该模型具有较高的准确率和较低的计算复杂度,可以有效提高滚动轴承故障诊断的准确性和可靠性。
中图分类号:
| [1]LIU R,YANG B,ZIO E,et al.Artificial intelligence for faultdiagnosis of rotating machinery:A review[J].Mechanical Systems and Signal Processing,2018,108:33-476. [2]YOUNG T,HAZARIKA D,PORIA S,et al.Recent trends in deep learning based natural language processing[J].IEEE Computational Intelligence Magazine,2017,13(3):55-75. [3]ZHAO R,YAN R Q,CHEN Z H,et al.Deep learning and itsap-plications to machine health monitoring[J].Mechanical Systems and Signal Processing,2019,115:213-237. [4]HUANG X D,PANG X W.Review of Intelligent Device FaultDiagnosis Based on Deep Learning[J/OL].Computer Science:1-12.[2023-05-06].http://kns.cnki.net/kcms/detail/50.1075.TP.20230214.1354.022.html. [5]JACOBS W,HOOREWEDER B V,BOONEN R,et al.The influence of external dynamic loads on the lifetime of rolling element bearings:Experimental analysis of the lubricant film and surface wear[J].Mechanical Systems and Signal Processing,2016,74(1):144-164. [6]LEI Y,YANG B,JIANG X,et al.Applications of machine learning to machine fault diagnosis:A review and roadmap[J].Mechanical Systems and Signal Processing,2020,138:106587. [7]DING Y,ZHUANG J,DING P,et al.Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings[J].Reliability Engineering & System Safety,2022,218:108126. [8]GAO Z,CECATI C,DING S,A survey of fault diagnosis and fault-tolerant techniques－part i:Fault diagnosis with model-based and signal-based approaches[J].IEEE Transactions on Industrial Electronics,2015(62):3757-3767. [9]ZHAO Z B,LI T F,WU J Y,et al.Deep learning algorithms for rotating machinery intelligent diagnosis:An open source benchmark study[J].ISA Transactions,2020,107:224-255. [10]LI Y,XU M,WEI Y,et al.A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree[J].Measurement,2016,77(Supplement C):80-94. [11]ZHANG W,PENG G,LI C,et al.A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals[J].Sensors(Basel),2017,17(2):425. [12]KIRANYAZ S,AVCI O,ABDELJABERO,et al.1D Convolutional Neural Networks and Applications:A Survey[J].arXiv:1905.03554,2019. [13]WU C Z,JIANG P C,FENG F Z,et al.Faults diagnosis method for gearboxes based on a 1-D convolutional neural network[J].Journal of Vibration and Shock,2018,37(22):51-56. [14]LIU Y,CHENG Q,SHI Y W,et al.Fault diagnosis of Rolling bearings based on attention module and 1D-CNN[J].Acta Solar Energy,2012,43(3):462-468. [15]ZHOU X K,YU J B.Gearbox Fault Diagnosis Based on One-dimension Residual Convolutional Auto-encoder[J].Journal of Mechanical Engineering,2020,56(7):96-108. [16]GUO M H,XU T X,LIU J J,et al.Attention mechanisms in computer vision:A survey[J].Computational Visual Media,2022,8(3):331-368. [17]HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv:1704.04861,2017. [18]GUO M H,XU T X,LIU J J,et al.Attention mechanisms in computer vision:A survey[J].Computational Visual Media,2022,8(3):331-368. [19]QIAN S,NING C,HU Y.MobileNetV3 for Image Classification[C]//2021 IEEE 2nd International Conference on Big Data,Artificial Intelligence and Internet of Things Engineering(ICBAIE).Nanchang,China,2021:490-497. [20]The case western reserve university bearing data center.Bearing data center fault test data[EB/OL].(1998-10-04).http://csegroups.case.edu/bearingdatacenter/home. |
|
||
首页