Fault diagnosis in rotating machinery presents unique challenges due to the limited availability of effective samples, which can lead to a long-tail effect during model training. To overcome these challenges, this paper introduces a novel small-sample fault diagnosis model for rotating machinery named the parallel convolution and bidirectional gated recurrent unit of attention model (PConvBiGA), which focuses on spatial and temporal feature extraction. The model is comprised of two core components: the wide convolutional denoising block (WCDB) and the hybrid attention mechanism spatio-temporal feature mining model (HAM-STM). The WCDB employs a one-dimensional wide convolutional layer coupled with a parallel pooling strategy, which helps preserve essential information from the original vibration data while filtering out weak noise and irrelevant features for diagnosis. To counter the feature loss typically encountered in traditional spatio-temporal feature fusion methods, the HAM-STM is developed. This structure utilizes a hybrid attention mechanism to effectively allocate high-dimensional feature weights across different paths, enhancing the model’s ability to mine features from various channels’ spatio-temporal characteristics. This, in turn, boosts the feature extraction capabilities of the model. Experimental results demonstrate that PConvBiGA successfully extracts more comprehensive features from vibration signals, enabling precise diagnosis with small samples and showing strong generalization and robustness.
KoTParkJJangJ, et al. Adaptive bearing fault diagnosis using reference frequency and modified scalogram. Struct Health Monit2024; 23(5): 2781–2794.
2.
HanTZhouTXiangY, et al. Cross-machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer. Struct Control Health Monit 2022; 29: e2898.
3.
SharmaVKunduP.Detection of seeded fault and growing fault in rotating machinery under different speed scenarios using adaptive resonance-based signal sparse decomposition. Struct Health Monit2024. DOI:10.1177/14759217241256130.
4.
TianJYiGWFeiCW, et al. Quantum entropy-based hierarchical strategy for intershaft bearing fault detection. Struct Control Health Monit2021; 12: 28.
5.
ZhongHYuSTrinhH, et al. A time-saving fault diagnosis using simplified fast GAN and triple-type data transfer learning. Struct Health Monit2024. DOI:10.1177/14759217241241985.
6.
PengBWanSBiY, et al. Automatic feature extraction and construction using genetic programming for rotating machinery fault diagnosis. IEEE Trans Cybernet2021; 51: 4909–4923.
7.
PanQBaoYLiH.Transfer learning-based data anomaly detection for structural health monitoring. Struct Health Monit2023; 22(5): 3077–3091.
8.
HouWZhangCJiangY, et al. A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data. Measurement2023; 215: 112879.
9.
XuYBaoYZhangY, et al. Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer. Struct Health Monit2021; 20(4): 1494–1517.
10.
YangTTangTWangJ, et al. A novel cross-domain fault diagnosis method based on model agnostic meta-learning. Measurement2022; 199: 111564.
11.
RanranLIShunmingLIKunXU, et al. Auxiliary generative mutual adversarial networks for class-imbalanced fault diagnosis under small samples. Chin J Aeronaut2022; 36; 464–478.
12.
PengBBiYXueB, et al. Multi-view feature construction using genetic programming for rolling bearing fault diagnosis [Application Notes]. IEEE Comput Intell Mag2021; 163: 79–94.
13.
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.
14.
SuHXiangLHuA, et al. A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions. Mech Syst Signal Proc2022; 169: 108765.
15.
KuoP-HLuanP-CTsengY-R, et al. Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets. Struct Health Monit2023; 22(6): 3891–3907.
16.
HuangFZhangKLiZ, et al. A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data. Struct Health Monit2024. DOI: 10.1177/14759217241248209.
17.
GuJPengYLuH, et al. A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN. Measurement2022; 200: 111635.
18.
ChengYLinMWuJ, et al. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowl Based Syst2021; 216: 106796.
19.
SuKLiuJXiongH.Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions. Knowl Based Syst2021; 230: 107386.
20.
ZhaoBZhangXLiH, et al. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions. Knowl Based Syst2020; 199: 105971.
21.
YuanCWangSQiY, et al. Automated structural bolt looseness detection using deep learning-based prediction model. Struct Control Health Monit2022; 3: 29.
22.
Soleimani-BabakamaliMHSoleimani-BabakamaliRSarloR.A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features. Struct Health Monit2022; 21(3): 1167–1182.
23.
TianHLiRYangL.Operation status monitoring of reciprocating compressors based on the fusion of spatio-temporal multiple information. Measurement2022; 204: 112087.
24.
ChiYCaiCRenJ, et al. Damage location diagnosis of frame structure based on wavelet denoising and convolution neural network implanted with inception module and LSTM. Struct Health Monit2024; 23(1): 57–76.
25.
ZhangWTengFLiJ, et al. Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing. Measurement2023; 216: 112951.
26.
MaMMaoZ.Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery. Struct Health Monit2021; 20(4): 1794–1804.
27.
WangSChenJWangH, et al. Degradation evaluation of slewing bearing using HMM and improved GRU. Measurement2019; 146: 385–395.
28.
ChengYXueJLuM, et al. MS-DenseNet-GRU tool wear prediction method based on attention mechanism. Struct Health Monit2024. DOI: 10.1177/14759217241240663.
29.
KordSTaghikhanyTAkbariM.A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection. Struct Health Monit2023; 23(4): 2270–2287.
30.
ShangYTangXZhaoG, et al. A remaining life prediction of rolling element bearings based on a bidirectional gate recurrent unit and convolution neural network. Measurement2022; 202: 111893.
31.
LvHChenJPanT, et al. Attention mechanism in intelligent fault diagnosis of machinery: a review of technique and application. Measurement2022; 199: 111594.
32.
WooSParkJLeeJ Y, et al. Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 8–14 September 2018, pp. 3–19.
33.
WeiZ.Research on Bearing Fault Diagnosis Algorithm Based on Convolutional Neural Network. Harbin, China: Harbin Institute of Technology, 2017.
34.
ZhangSLiuZChenY, et al. Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. ISA Trans2023; 133: 369–383.
35.
ZhangXHeCLuY, et al. Fault diagnosis for small samples based on attention mechanism. Measurement2022; 187: 110242.
36.
ZhaoBZhangXZhanZ, et al. Deep multi-scale convolutional transfer learning network: a novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains. Neurocomputing2020; 407: 24–38.
37.
CaoPZhangSTangJ.Preprocessing-free gear fault diagnosis using small datasets, with deep convolutional neural network-based transfer learning. IEEE Access2018; 6: 26241–26253.
38.
ZhangGLiYJiangW, et al. A fault diagnosis method for wind turbines with limited labeled data based on balanced joint adaptive network. Neurocomputing2022; 481: 133–153.
39.
MirzaeiSKangJ-LChuK-Y.A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization. J Taiwan Inst Chem Eng2022; 130: 104028.
40.
WangZDongYLiuW, et al. A novel fault diagnosis approach for chillers based on 1-D convolutional neural network and gated recurrent unit. Sensors2020; 20: 2458.
41.
ShiJPengDPengZ, et al. Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks. Mech Syst Signal Proc2022; 162: 107996.
42.
ChaoZHanT.A novel convolutional neural network with multiscale cascade midpoint residual for fault diagnosis of rolling bearings. Neurocomputing2022; 506: 213–227.
