Existing intelligent fault diagnosis approaches demand substantial data for training diagnostic models. However, factors such as the inherent characteristics of bearings, operating conditions, and privacy security make collecting comprehensive fault-bearing data very difficult. Although generating synthetic data through generative adversarial networks (GANs) is feasible, the data generation of GANs is a time-consuming process. To address these challenges, a fault diagnosis framework based on GAN and deep transfer learning (DTL) is proposed, termed the simplified fast GAN triple-type data transfer learning (SFGAN-TDTL) method. Initially, an SFGAN is proposed as a replacement for traditional GANs. The time-frequency image data generated by SFGAN serve to augment the training dataset, offering faster and higher-quality data generation compared to traditional GANs. To further reduce time consumption for GAN-based methods, the TDTL method is proposed. Differing from DTL, which utilizes synthetic data to construct a pre-trained model and conducts targeted fine-tuning with real data, TDTL employs open-source data, synthetic data, and real data to fill the weights of the task-insensitive layer, task-sensitive layer, and fully connected layer, respectively. Numerical results demonstrate that SFGAN-TDTL maintains higher diagnostic accuracy while significantly reducing time consumption.
LiWHuangRLiJ, et al. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: theories, applications and challenges. Mech Syst Signal Process2022; 167: 108487.
2.
HeZShaoHZhongX, et al. Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions. Knowl Based Syst2020; 207: 106396.
3.
LvYYuanRSongG. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing. Mech Syst Signal Process81 (2016): 219–34.
4.
WeiDHanTChuF, et al. Weighted domain adaptation networks for machinery fault diagnosis. Mech Syst Signal Process2021; 158: 107744.
5.
LeiYYangBJiangX, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process2020; 138: 106587.
6.
RuanDWangJYanJ, et al. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv Eng Inform2023; 55: 101877.
7.
YuanRLvYKongQ, et al. Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and BP neural network. Smart Mater Struct2019; 28(12): 125001.
8.
MaYLvYYuanR, et al. Synchro spline-kernelled chirplet extracting transform: a useful tool for characterizing time-varying features under noisy environments and applications to bearing fault diagnosis. Meas J Int Meas Confed2021; 181: 109574.
9.
JiaoJZhaoMLinJ, et al. A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing2020; 417: 36–63.
10.
LvSLvYYuanR, et al. High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis. Mech Syst Signal Process2022; 172: 108959.
11.
WidodoAYangB-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process2007; 21(6): 2560–2574.
12.
CerradaMZuritaGCabreraD, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech Syst Signal Process2016; 70: 87–103.
13.
SunWChenJLiJ. Decision tree and PCA-based fault diagnosis of rotating machinery. Mech Syst Signal Process2007; 21(3): 1300–1317.
14.
ShaoHJiangHZhangX, et al. Rolling bearing fault diagnosis using an optimization deep belief network. Meas Sci Technol2015; 26(11): 115002.
15.
QiYShenCWangD, et al. Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery. IEEE Access2017; 5: 15066–15079.
16.
ZhangWLiXMaH, et al. Federated learning for machinery fault diagnosis with dynamic validation and self-supervision. Knowl Based Syst2021; 213: 106679.
17.
ChenEWangZZhengJ. A zero-sample industrial process fault diagnosis model based on joint explicit and implicit attribute transfer. Measurement2023; 218: 113236.
18.
ShiHSongZBaiX, et al. A novel digital twin model for dynamical updating and real-time mapping of local defect extension in rolling bearings. Mech Syst Signal Process2023; 193: 110255.
19.
GoodfellowIPouget-AbadieJMirzaM, et al. Generative adversarial networks. Commun ACM2020; 63(11): 139–144.
20.
ZhangTChenJLiF, et al. Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA Trans2022; 119: 152–171.
21.
LiWZhongXShaoH, et al. Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework. Adv Eng Inform2022; 52: 101552.
22.
ZhongHYuSTrinhH, et al. Fine-tuning transfer learning based on DCGAN integrated with self-attention and spectral normalization for bearing fault diagnosis. Meas J Int Meas Confed2023; 210: 112421.
23.
ZhouFYangSFujitaH, et al. Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowl Based Syst2020; 187: 104837.
24.
WanWHeSChenJ, et al. QSCGAN: an un-supervised quick self-attention convolutional GAN for LRE bearing fault diagnosis under limited label-lacked data. IEEE Trans Instrum Meas2021; 70: 1–16.
25.
FanJYuanXMiaoZ, et al. Full attention Wasserstein GAN with gradient normalization for fault diagnosis under imbalanced data. IEEE Trans Instrum Meas2022; 71: 1–16.
26.
WuYTangBDengL, et al. Distillation-enhanced fast neural architecture search method for edge-side fault diagnosis of wind turbine gearboxes. Expert Syst Appl2022; 208: 118049.
27.
SepahvandMAbdali-MohammadiFTaherkordiA. Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications. Expert Syst Appl2022; 202: 117474.
28.
TangTQiuCYangT, et al. A novel lightweight relation network for cross-domain few-shot fault diagnosis. Measurement2023; 213: 112697.
29.
KennewegPStallmannDHammerB. Novel transfer learning schemes based on siamese networks and synthetic data. Neural Comput Appl2022; 35(11): 8423–8436.
30.
LiXJiangHXieM, et al. A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains. Adv Eng Inform2022; 51: 101480.
31.
DongYLiYZhengH, et al. A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: solving the small sample problem. ISA Trans2022; 121: 327–348.
32.
ZhongXBanH. Pre-trained network-based transfer learning: a small-sample machine learning approach to nuclear power plant classification problem. Ann Nucl Energy2022; 175: 109201.
33.
LiYJiangWZhangG, et al. Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data. Renew Energy2021; 171: 103–115.
34.
XuYSunYLiuX, et al. A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access2019; 7: 19990–19999.
35.
LiCZhangSQinY, et al. A aystematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing2020; 407: 121–135.
36.
ChenYWenXZhangY, et al. CCPrune: collaborative channel pruning for learning compact convolutional networks. Neurocomputing2021; 451: 35–45.
37.
ShaoSMcAleerSYanR, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform2019; 15(4): 2446–2455.
38.
LiuBZhuYSongK, et al. Towards faster and stabi-lized GAN training for high-fidelity few-shot image synthesis. arXiv preprint arXiv:2101.04775v1, 2021.
39.
LiXZhangWDingQ, et al. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. J Intell Manuf2020; 31(2): 433–452.
40.
WangJFuPJiS, et al. A light weight multisensory fusion model for induction motor fault diagnosis. IEEE/ASME Trans Mechatron2022; 27(6): 4932–4941.
41.
SmithWARandallRB. Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech Syst Signal Process2015; 64–65: 100–131.
42.
DuYChenYMengG, et al. Fault severity monitoring of rolling bearings based on texture feature extraction of sparse time–frequency images. Appl Sci2018; 8(9): 1538.
43.
PanTChenJZhangT, et al. Generative adversarial network in mechanical fault diagnosis under small sample: a systematic review on applications and future perspectives. ISA Trans2022; 128: 1–10.
44.
WenLLiXGaoL. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput Appl2020; 32: 6111–6124.
45.
GaoXDengFYueX. Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty. Neurocomputing2020; 396: 487–494.
46.
YangJLiuJXieJ, et al. Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples. IEEE Trans Instrum Meas2021; 70: 1–12.
47.
LiangPDengCWuJ, et al. Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network. Measurement2020; 159: 107768.
48.
YangXLiuBXiangL, et al. A novel intelligent fault diagnosis method of rolling bearings with small samples. Measurement2022; 203: 111899.
