The vibration signal of the bearing is mostly used in the fault diagnosis research of rolling bearings, and the vibration signal has the characteristics of strong non-smoothness and is easy to be disturbed by noise. This characteristic of the vibration signal has a strong adverse effect on the fault diagnosis of bearings. To improve the accuracy of bearing fault diagnosis, a rolling bearing fault diagnosis method based on the combination of a short-time Fourier transform and a convolutional neural network is proposed. Firstly, a short-time Fourier transform is performed on the original vibration signal of the bearing to obtain the time-frequency image of the vibration signal. Then, the dual model of 2D convolutional neural network which can obtain abundant fault information is built. Secondly, the obtained time-frequency image is image compressed to fit the proposed model. Finally, the images are input into the network for fault diagnosis, and the fault classification accuracy reaches 100%. The proposed model is tested by two methods: adding Gaussian noise to the time-frequency image and testing the model using bearing data from other working conditions, and the results show that the proposed method has good noise immunity and generalization, which can provide an effective diagnostic scheme for bearing fault diagnosis.
YuanLLianDKangX, et al. Rolling bearing fault diagnosis based on convolutional neural network and support vector machine. IEEE Access2020; 8: 137395–137406.
2.
NieH. Overview of rolling bearing fault diagnosis methods. Internal Combust Engine Access2019; 23: 149–150.
3.
HuJDengS. Rolling bearing fault diagnosis based on wireless sensor network data fusion. Comput Commun2022; 181: 404–411.
4.
ZhaoLZhangYZhuD. Summary of research on fault diagnosis and prediction methods of rolling bearings in complex equipment. China Test2020; 46(3): 17–25.
5.
LiYChengGPangY, et al. Planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum. Sensors2018; 18(6): 1735.
6.
LeiYYangBJiangX, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process2020; 138: 106587.
7.
ZhuZLeiYQiG, et al. A review of the application of deep learning in intelligent fault diagnosis of rotating machinery. Measurement2023; 206: 112346.
8.
TahaMMRNoureldinALuceroJL, et al. Wavelet transform for structural health monitoring: a compendium of uses and features. Struct Health Monit2006; 5(3): 267–295.
9.
PatelDPatelSPatelP, et al. Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: a comprehensive and systematic study. Environ Sci Pollut Res2022; 29(22): 32428–32442.
10.
GoyalDChoudharyAPablaBS, et al. Support vector machines based non-contact fault diagnosis system for bearings. J Intell Manuf2020; 31: 1275–1289.
11.
WanLGongKZhangG, et al. An efficient rolling bearing fault diagnosis method based on spark and improved random forest algorithm. IEEE Access2021; 9: 37866–37882.
12.
MoosavianAAhmadiHTabatabaeefarA, et al. Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing. Shock Vib2013; 20(2): 263–272.
13.
HintonGEOsinderoSTehY-W. A fast learning algorithm for deep belief nets. Neural Comput2006; 18(7): 1527–1554.
14.
YanYCaiJTangY, et al. A decentralized Boltzmann-machine-based fault diagnosis method for sensors of air handling units in HVACs. J Build Eng2022; 50: 104130.
15.
LiuHZhouJZhengY, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans2018; 77: 167–178.
16.
AnYZhangKLiuQ, et al. Rolling bearing fault diagnosis method base on periodic sparse attention and LSTM. IEEE Sens J2022; 22(12): 12044–12053.
17.
WangJWangDWangS, et al. Fault diagnosis of bearings based on multi-sensor information fusion and 2D convolutional neural network. IEEE Access2021; 9: 23717–23725.
18.
HeKZhangXRenS, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell2015; 37(9): 1904–1916.
19.
KrizhevskyASutskeverIHintonGE. Imagenet classification with deep convolutional neural networks. Commun ACM2017; 60(6): 84–90.
20.
WangHXuJYanR, et al. Intelligent bearing fault diagnosis using multi-head attention-based CNN. Procedia Manuf2020; 49: 112–118.
21.
WangDGuoQSongY, et al. Application of multiscale learning neural network based on CNN in bearing fault diagnosis. J Signal Process Syst2019; 91: 1205–1217.
22.
ZhangYXingKBaiR, et al. An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement2020; 157: 107667.
23.
ZhongDGuoWHeD. (eds) An intelligent fault diagnosis method based on STFT and convolutional neural network for bearings under variable working conditions. In: 2019 Prognostics and system health management conference (PHM-Qingdao), 2019. New York, NY: IEEE.
24.
ZhouSXiaoMBartosP, et al. Remaining useful life prediction and fault diagnosis of rolling bearings based on short-time fourier transform and convolutional neural network. Shock Vib2020; 2020: 1–14.
25.
VerstraeteDFerradaADroguettEL, et al. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock Vib2017; 2017: 1–17.
26.
NiQJiJCFengK. Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network. IEEE Trans Ind Inform2023; 19(2): 1301–1311.
27.
WooSParkJLeeJ-Y, et al. (eds). Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), 2018.
28.
GuoYMaoJZhaoM. Rolling bearing fault diagnosis method based on attention CNN and Bilstm network. Neural Process Lett2023; 55: 3377–3410.
29.
WangCLiBJiaoB. Fault diagnosis of rolling bearing based on convolutional neural network of convolutional block attention module. J Phys Conf Ser2021; 1732(1): 012045.
30.
YuMLiuHWangR, et al. (eds). An improved CNN based on attention mechanism with multi-domain feature fusion for bearing fault diagnosis. 2021 IEEE International Conference on Prognostics and Health Management (ICPHM); 2021. New York, NY: IEEE.
31.
YeZYuJ. Multi-level features fusion network-based feature learning for machinery fault diagnosis. Appl Soft Comput2022; 122: 108900.
32.
WangJYeLGaoRX, et al. Digital twin for rotating machinery fault diagnosis in smart manufacturing. Int J Prod Res2019; 57(12): 3920–3934.
33.
XiaMShaoHWilliamsD, et al. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning. Reliab Eng Syst Saf2021; 215: 107938.
34.
WangLHZhaoXPWuJX, et al. Motor fault diagnosis based on short-time Fourier transform and convolutional neural network. Chin J Mech Eng2017; 30(6): 1357–1368.
35.
LecunYBottouLBengioY, et al. Gradient-based learning applied to document recognition. Proc IEEE1998; 86(11): 2278–2324.
