Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700167-6.doi: 10.11896/jsjkx.230700167

• Interdiscipline & Application • Previous Articles     Next Articles

Intelligent Fault Diagnosis Method for Rolling Bearing Based on SAMNV3

ZHANG Lanxin, XIANG Ling, LI Xianze, CHEN Jinpeng   

  1. Department of Mechanical Engineering,North China Electric Power University,Baoding,Hebei 071003,China
  • Published:2024-06-06
  • About author:ZHANG Lanxin,born in 1998,postgraduate.Her main research interests include machine learning,deep learning,and fault diagnosis and classification.
    XIANG Ling,born in 1971,Ph.D,professor,Ph.D supervisor.Her main research interests include vibration analysis and fault diagnosis,intelligent ope-ration and maintenance,dynamics research.
  • Supported by:
    National Natural Science Foundation of China(52075170,52175092).

PDF (PC)

268

Abstract: In order to accurately identify the fault categories of rolling bearings,which are essential components of mechanical equipment,this paper proposes a SAMNV3 intelligent fault diagnosis model for rolling bearings that integrates the self-attention(SA) mechanism and the lightweight network MobileNetV3.This model takes advantage of the adaptive weighting of the features by the self-attention mechanism and the small size of the lightweight network MobileNetV3 to achieve end-to-end rolling bearing intelligent fault diagnosis by directly inputting the original vibration signals from two different datasets into the SAMNV3 model for feature extraction and fault identification and classification.The results of the validation of the two different datasets show that the model has high accuracy and low computational complexity,which can effectively improve the accuracy and reliability of rolling bearing fault diagnosis.

Key words: Rolling bearing, Intelligent fault diagnosis, Self-attention mechanism, Lightweight network, MobileNetV3

CLC Number: 

  • TH165.3
[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.
[1] LIU Xiaohu, CHEN Defu, LI Jun, ZHOU Xuwen, HU Shan, ZHOU Hao. Speaker Verification Network Based on Multi-scale Convolutional Encoder [J]. Computer Science, 2024, 51(6A): 230700083-6.
[2] QUE Yue, GAN Menghan, LIU Zhiwei. Object Detection with Receptive Field Expansion and Multi-branch Aggregation [J]. Computer Science, 2024, 51(6A): 230600151-6.
[3] HE Xinyu, LU Chenxin, FENG Shuyi, OUYANG Shangrong, MU Wentao. Ship Detection and Recognition of Optical Remote Sensing Images for Embedded Platform [J]. Computer Science, 2024, 51(6A): 230700117-7.
[4] ZHANG Feng, HUANG Shixin, HUA Qiang, DONG Chunru. Novel Image Classification Model Based on Depth-wise Convolution Neural Network andVisual Transformer [J]. Computer Science, 2024, 51(2): 196-204.
[5] TENG Sihang, WANG Lie, LI Ya. Non-autoregressive Transformer Chinese Speech Recognition Incorporating Pronunciation- Character Representation Conversion [J]. Computer Science, 2023, 50(8): 111-117.
[6] YAN Mingqiang, YU Pengfei, LI Haiyan, LI Hongsong. Arbitrary Image Style Transfer with Consistent Semantic Style [J]. Computer Science, 2023, 50(7): 129-136.
[7] LI Fan, JIA Dongli, YAO Yumin, TU Jun. Graph Neural Network Few Shot Image Classification Network Based on Residual and Self-attention Mechanism [J]. Computer Science, 2023, 50(6A): 220500104-5.
[8] DOU Zhi, HU Chenguang, LIANG Jingyi, ZHENG Liming, LIU Guoqi. Lightweight Target Detection Algorithm Based on Improved Yolov4-tiny [J]. Computer Science, 2023, 50(6A): 220700006-7.
[9] HUANG Xundi, PANG Xiongwen. Review of Intelligent Device Fault Diagnosis Based on Deep Learning [J]. Computer Science, 2023, 50(5): 93-102.
[10] WANG Xianwang, ZHOU Hao, ZHANG Minghui, ZHU Youwei. Hyperspectral Image Classification Based on Swin Transformer and 3D Residual Multilayer Fusion Network [J]. Computer Science, 2023, 50(5): 155-160.
[11] YANG Bin, LIANG Jing, ZHOU Jiawei, ZHAO Mengci. Study on Interpretable Click-Through Rate Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(5): 12-20.
[12] JIANG Bo, WAN Yi, XIE Xianzhong. Improved YOLOv5s Lightweight Steel Surface Defect Detection Model [J]. Computer Science, 2023, 50(11A): 230900113-7.
[13] ZHANG Dehui, DONG Anming, YU Jiguo, ZHAO Kai andZHOU You. Speech Enhancement Based on Generative Adversarial Networks with Gated Recurrent Units and Self-attention Mechanisms [J]. Computer Science, 2023, 50(11A): 230200203-9.
[14] CHEN Jiajun, CHEN Wei, ZHAO Lei. Road Network Topology-aware Trajectory Representation Learning [J]. Computer Science, 2023, 50(11): 114-121.
[15] ZHANG Jingyuan, WANG Hongxia, HE Peisong. Multitask Transformer-based Network for Image Splicing Manipulation Detection [J]. Computer Science, 2023, 50(1): 114-122.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.