With the development of deep learning, convolution neural network (CNN)-based classifiers have already been demonstrated as a powerful method to detect bearing fault in wavelet-transformed time-frequency images. However, the real-world motor operations that suffer from concurrently occurred bearing faults have to define the fault labels that take all possible bearing fault combinations into account. Such a large number of labels significantly increase the labor and time costs in dataset labeling and network training. In this study, the CNN-based object detection is suggested to perform concurrent bearing faults diagnosis instead of conventional classifiers. Since object detection is capable of identifying multiple individual bearing faults independently in a single time-frequency image, it only requires to define the individual bearing fault labels rather than all possible bearing fault combinations. It dramatically reduces the workload in dataset preparation. In addition, to address the issue of relatively high noise level in weak abnormal vibrations, a moving average scheme is applied on the time-domain vibration signal in advance of time-frequency image generation. With the assistance of the moving average, the noise level is effectively suppressed, and x times higher detection accuracy is achieved in concurrent bearing fault diagnosis.
BochkovskiyAWangCYLiaoHYM (2014) YOLOv4: Optimal speed and accuracy of object detection. Available at: https://arxiv.org/abs/2004.10934
3.
CheCWangHNiX, et al. (2020) Domain adaptive deep belief network for rolling bearing fault diagnosis. Computers & Industrial Engineering143: 106427.
4.
ChenXHYangRXueYH, et al. (2023) Deep transfer learning for bearing fault diagnosis: A systematic review since 2016. IEEE Transactions on Instrumentation and Measurement72: 3508221.
5.
ChenZCenJXiongJ (2020) Rolling bearing fault diagnosis using time-frequency analysis and deep transfer convolutional neural network. IEEE Access8: 150248–150261.
6.
DhamandeLSChaudhariMB (2018) Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement125: 63–77.
HanKWangYTianQ, et al. (2020) Ghostnet: More features from cheap operations. In: Proceedings of the 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Seattle, WA, 13–19 June, pp. 1577–1586. Washington, DC: IEEE Computer Society.
9.
HaoSGeFLiY, et al. (2020) Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Measurement159: 107802.
10.
HeKZhangXRenS, et al. (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, 27–30 June, pp. 770–778. Washington, DC: IEEE Computer Society.
11.
HsuehYMIttangihalVRWuWB, et al. (2019) Fault diagnosis system for induction motors by CNN using empirical wavelet transform. Symmetry11: 1212.
12.
KankarPKSharmaSCHarshaSP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing11(2): 2300–2312.
13.
KonarPChattopadhyayP (2015) Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform. Applied Soft Computing30: 341–352.
14.
KongXMaoGWangQ, et al. (2020) A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings. Measurement151: 107132.
15.
LeiYYangBJiangX, et al. (2020) Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing138: 106587.
16.
LiuBWuQLiZ, et al. (2021) Research on fault diagnosis of IPMSM for electric vehicles based on multi-level feature fusion SPP network. Symmetry13: 1844.
17.
LiuRNYangBYZhangXL, et al. (2016) Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mechanical Systems and Signal Processing75: 345–370.
18.
LouXSLoparoK (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing18(5): 1077–1095.
19.
MaJHLiHLTangBP, et al. (2022) Rolling bearing fault diagnosis based on improved VMD-adaptive wavelet threshold joint noise reduction. Advances in Mechanical Engineering14(10): 1–16.
20.
MaoWTDingLTianSY, et al. (2020) Online detection for bearing incipient fault based on deep transfer learning. Measurement152: 107278.
21.
NeupaneDKimYSeokJ (2021) Bearing fault detection using scalogram and switchable normalization-based CNN (SN-CNN). IEEE Access9: 88151–88166.
22.
PengZKTsePWChuFL (2005) A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mechanical Systems and Signal Processing19(5): 974–988.
23.
PlakiasSBoutalisYS (2020) Fault detection and identification of rolling element bearings with Attentive Dense CNN. Neurocomputing405: 208–217.
24.
PuZQCabreraDBaiY, et al. (2022) A one-class generative adversarial detection framework for multifunctional fault diagnoses. IEEE Transactions on Industrial Electronics69(8): 8411–8419.
RenSHeKGirshickR, et al. (2017) Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence39(6): 1137–1149.
27.
ShaoHJiangHLinY, et al. (2018) A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing102: 278–297.
28.
SimonyanKZissermanA (2014) Very deep convolutional networks for large-scale image recognition. Available at: https://arxiv.org/abs/1409.1556
29.
SinghGKAl Kazzaz Sa’adAhmedS (2004) Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electric Power Systems Research68(2): 119–136.
30.
SzkulmowskiMWojtkowskiM (2013) Averaging techniques for OCT imaging. Optics Express21: 9757–9773.
31.
VerstraeteDFerradaADroguettEL, et al. (2017) Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock and Vibration2017: 5067651.
32.
WangDGuoQSongY, et al. (2019) Application of multiscale learning neural network based on CNN in bearing fault diagnosis. Journal of Signal Processing Systems91(10): 1205–1217.
33.
WangYHuangYHYangK, et al. (2022) Generator fault classification method based on multi-source information fusion Naïve Bayes classification algorithm. Energies15(24): 9635.
34.
WenLLiXGaoL (2020) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Computing and Applications32(10): 6111–6124.
35.
WenLLiXGaoL, et al. (2017) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics65: 5990–5998.
36.
WenLLiXLiXY, et al. (2019) A new transfer learning based on VGG-19 network for fault diagnosis. In: Proceedings of the 2019 IEEE 23rd international conference on computer supported cooperative work in design (CSCWD), Porto, 6–8 May, pp. 205–209. New York: IEEE.
37.
XiaoDHuangYZhaoL, et al. (2019) Domain adaptive motor fault diagnosis using deep transfer learning. IEEE Access7: 80937–80949.
38.
YangWLZhangYWDongY, et al. (2022) Development of machine vision system for off-line inspection of fine defects on glass screen surface. IEEE Transactions on Instrumentation and Measurement71: 5016008.
39.
ZhangJZhouYWangB, et al. (2021) Bearing fault diagnosis base on multi-scale 2D-CNN model. In: Proceedings of the 2021 3rd international conference on machine learning, big data and business intelligence (MLBDBI), Taiyuan, China, 3–5 December, pp. 72–75. New York: IEEE.
40.
ZhangSZhangSBWangBN, et al. (2020) Deep learning algorithms for bearing fault diagnostics—A comprehensive review. IEEE Access8: 29857–29881.
41.
ZhangWLiCPengG, et al. (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing100: 439–453.
