A convolutional neural network fault diagnosis method based on frequency attention mechanism was designed for the problem that the traditional method cannot adaptively extract effective feature information in rolling bearing fault diagnosis and the diagnosis effect of rolling bearing is poor under strong environmental noise interference. Firs, the Mel-frequency cepstral coefficient (MFCC) of the bearing vibration signal was extracted. Second, to solve the problem of the channel attention mechanism adopting global average pooling (GAP) and neglecting channel internal characteristic information, the GAP was extended in the frequency domain, and a two-stage frequency component selection criterion was designed. The results show that the MFCC method can extract fault-sensitive features in industrial noise environments, improve the existing channel attention mechanism using frequency domain attention mechanism, and overcome the information loss caused by GAP of convolutional layer features in channel attention mechanism. Identification accuracy, recall rate, and F1-score are 100% on the rolling bearing simulation fault datasets of Case Western Reserve University and Central South University. Compared with the convolutional block attention module, the accuracy of the method combining spatial attention mechanism and channel attention mechanism is improved by 0.34 and 0.24%, respectively, and compared with other front-bearing fault diagnosis methods, it also offers significant improvement.
NandiSToliyatHLiX. Condition monitoring and fault diagnosis of electrical motors–a review. IEEE Trans Energy Convers2005; 20(4): 719–729.
2.
ZhaoXJiaM. A new local-global deep neural network and its application in rotating machinery fault diagnosis. Neurocomputing2019; 366: 215–233.
3.
ZhaoXJiaMLinM. Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement2020; 152: 107320.
4.
KongYQinZWangT, et al. Data-driven dictionary design–based sparse classification method for intelligent fault diagnosis of planet bearings. Struct Health Monit2022; 21(4): 1313–1328.
5.
PhamDSVenkateshSLazarescuM, et al. Anomaly detection in large-scale data stream networks. Data Min Knowl Discov2014; 28(1):145–189.
6.
ChenSXieBWangY, et al. Non-stationary harmonic summation: a novel method for rolling bearing fault diagnosis under variable speed conditions. Struct Health Monit2023; 22(3): 1554–1580.
7.
MartinGSDroguettELMeruaneV, et al. Deep variational auto-encoders: a promising tool for dimensionality reduction and ball bearing elements fault diagnosis. Struct Health Monit2019; 18(4): 1092–1128.
8.
HoseinzadehMSKhademSESadooghiMS. Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition. ISA Trans2018; 83: 261–275.
9.
WangHHeWWuJ, et al. A coordinate-based meta-analysis of the n-back working memory paradigm using activation likelihood estimation. Brain Cogn2019; 132: 1–12.
10.
ZhangWLiCPengG, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process2018; 100: 439–453.
11.
LiXDingQSunJQ. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf2018; 172: 1–11.
12.
ChoudharyAMishraRKFatimaS, et al. Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor. Eng Appl Artif Intell2023; 120: 105872.
13.
LiXXuYLiN, et al. Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE/CAA J Autom Sin2023; 10: 121–134.
14.
HakimMOmranAInayat-HussainJ, et al. Bearing fault diagnosis using lightweight and robust one-dimensional convolution neural network in the frequency domain. Sensors2022; 22: 1424–8220.
15.
JiaoJZhaoMLinJ, et al. A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing2020; 417: 36–63.
16.
DuJGuiLHeY, et al. Convolution-based neural attention with applications to sentiment classification. IEEE Access2019; 7: 27983–27992.
17.
LiXChebiyyamVKirchhoffK. Multi-stream network with temporal attention for environmental sound classification. Preprint. arXiv: 1901.08608. doi: 10.48550/arXiv.1901.08608.
18.
HangJWuYLiY, et al. A deep learning semantic segmentation network with attention mechanism for concrete crack detection. Structural Health Monitoring2023; 22(5): 3006–3026.
19.
YaoYZhangSYangS, et al. Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions. Sensors2020; 20(4): 1233.
20.
ShiYDengADengM, et al. Enhanced lightweight multiscale convolutional neural network for rolling bearing fault diagnosis. IEEE Access2020; 8: 217723–217734.
21.
FuJLiuJTianH, et al. Dual attention network for scene segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 3141–3149. IEEE.
22.
XuKLiSJiangX, et al. A novel transfer diagnosis method under unbalanced sample based on discrete-peak joint attention enhancement mechanism. Knowl Based Syst2021; 212: 106645.
23.
WangHXuJYanR, et al. A new intelligent bearing fault diagnosis method using sdp representation and se-cnn. IEEE Trans Instrum Meas2020; 69(5): 2377–2389.
24.
HaoYWangHLiuZ, et al. Multi-scale CNN based on attention mechanism for rolling bearing fault diagnosis. In: 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, BC, Canada, 2020, pp. 1–5. IEEE.
25.
ChangYChenJChenQ, et al. CFs-focused intelligent diagnosis scheme via alternative kernels networks with soft squeeze-and-excitation attention for fast-precise fault detection under slow & sharp speed variations. Knowl Based Syst2022; 239: 108026.
26.
ZhongZLinZQBidartR, et al. Squeeze-and-attention networks for semantic segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 13062–13071. IEEE.
27.
WangQWuBZhuP, et al. ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020 pp. 11531–11539. IEEE.
28.
ZhangYLiKLiK, et al. Residual non-local attention networks for image restoration. 2019, Preprint. arXiv:1903.10082. DOI: 10.48550/arXiv.1903.10082.
29.
LiXYuSLeiY, et al. Intelligent machinery fault diagnosis with event-based camera. IEEE Trans Ind Inform2023; 1–10. DOI: 10.1109/TII.2023.3262854.
30.
WangBHanTLeiY, et al. Remaining useful life prediction based on deep residual attention network. In: 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Beijing, China, 2019, pp. 79–84. IEEE.
31.
JiangSXuanJDuanJ, et al. Dual attention dense convolutional network for intelligent fault diagnosis of spindle-rolling bearings. J Vib Control2020; 27: 2403–2419.
32.
MiaoMLiuCYuJ, et al. Adaptive densely connected convolutional auto-encoder-based feature learning of gearbox vibration signals. IEEE Transactions on Instrumentation and Measurement. 2021; 70: 1–11.
33.
YaoDLiuHYangJ, et al. Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism. J Intell Manuf2021; 32(3): 729–743.
34.
ChangYHChenJLHeSL. Intelligent fault diagnosis of satellite communication antenna via a novel meta-learning network combining with attention mechanism. J Phys Conf Ser2020; 1510: 12–26.
35.
YangJWangXZhangW, et al. Transient fault diagnosis of track circuit based on MFCC-DTW. In: 2019 CAA symposium on fault detection, supervision and safety for technical processes (SAFEPROCESS), Xiamen, China, 2019, pp. 379–384. IEEE.
36.
CaoJZhaoTWangJ, et al. Excavation equipment classification based on improved MFCC features and ELM. Neurocomputing2017; 261: 231–241.
37.
WangDGuoQSongY, et al. Application of multiscale learning neural network based on CNN in bearing fault diagnosis. J Signal Process Syst2019; 91(10): 1205–1217.
38.
HeKZhangXRenS, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778. IEEE.
39.
ParkJWooSLeeJY, et al. BAM: bottleneck attention module, 2018. doi.10.48550/arXiv.1807.06514.
40.
WooSParkJLeeJY, et al. CBAM: convolutional block attention module. In: Computer Vision ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, pp. 3–19. Berlin, Heidelberg: Springer-Verlag.
41.
LuqueACarrascoAMartínA, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognit2019; 91: 216–231.
42.
DengXLiuQDengY, et al. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci2016; 340–341: 250–261.
43.
MaatenLHintonGE. Visualizing data using t-sne. J Mach Learn Res2008; 9: 2579–2605.
44.
SelvarajuRRCogswellMDasA, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Proc IEEE Int Conf Comput Vis2016; 128: 336–359.
45.
SmithWARandallRB. Rolling element bearing diagnostics using the case Western Reserve University data: a benchmark study. Mech Syst Signal Process2015; 64–65: 100–131.
