Existing intelligent fault diagnosis methods based on two-dimensional time–frequency representation follow a single-view paradigm, which relies on adjusting convolutional or attention mechanisms for feature extraction and classification. However, this paradigm exhibits multiview perception deficiencies, including noise-masked fault impulses in the input view, corrupted timefrequency features caused by fixed padding and nonadaptive convolution in the operator view, and a lack of targeted guidance for difficult and easy samples in the training view. To address these deficiencies, we propose a multiview perception-driven network (MVPNet) for intelligent fault diagnosis. First, a noise-perception multiscale dilated gating modulator is designed to enhance noise perception in the input view. The modulator injects oriented perturbations guided by an adaptive noise-aware map and modulates them through multiscale dilated gating, forcing the network to distinguish fault impulses from noise. Second, to alleviate degraded edge perception in the operator view, a boundary-adaptive padding module (BAPM) and an edge-adaptive gradient-sensitive mask are designed to construct a boundary-edge perception module. This module preserves the topological integrity of feature boundaries through adaptive padding and enhances edge responses via gradient-sensitive masking. Finally, to overcome the absence of difficulty perception in the training view, a difficulty-perception cluster-guided inverse distillation (DCID) strategy is proposed. In this strategy, a difficulty memory mechanism is constructed, upon which intraclass multistate soft labels are generated through online clustering and inverse neighbor sampling to guide the learning of difficult and easy samples. Comprehensive experiments on the Case Western Reserve University and the self-built Anhui University of Science and Technology platforms demonstrate that MVPNet markedly surpasses existing mainstream methods.
FangBLiMZhangJ, et al. Comprehensive theoretical and experimental study on the transient slip behaviors of the roller and cage within the radial loaded cylindrical roller bearing. Mech Syst Signal Process2025; 235: 112887.
2.
LiXWangYZhangG, et al. Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions. Mech Syst Signal Process2024; 208: 110943.
3.
FanCZhangYMaH, et al. A novel metric-based model with the ability of zero-shot learning for intelligent fault diagnosis. Eng Appl Artif Intell2024; 129: 107605.
4.
HeDZhangZJinZ, et al. RTSMFFDE-HKRR: a fault diagnosis method for train bearing in noise environment. Measurement2025; 239: 115417.
5.
HeSZhouYYangY, et al. Cascading failure in cyber–physical systems: a review on failure modeling and vulnerability analysis. IEEE Trans Cybern2024; 54(12): 7936–7954.
6.
LiaoHXiePDengS, et al. Research on early fault intelligent diagnosis for oil-impregnated cage in space ball bearing. Expert Syst Appl2024; 238: 121952.
7.
PengBBiYXueB, et al. A survey on fault diagnosis of rolling bearings. Algorithms2022; 15(10): 347.
8.
PandiyanMBabuTN. Systematic review on fault diagnosis on rolling-element bearing. J Vib Eng Technol2024; 12(7): 8249–8283.
9.
ZhaoJSunXWangJ, et al. Fault diagnostics of AC motor bearings based on envelope analysis of vibration residual signal. Proc TEPEN2022; 129: 889–899.
10.
LaiLXuWSongZ. A novel fault diagnosis method for rolling bearings based on spectral kurtosis and LS-SVM. Electronics2025; 14(14): 2790.
11.
LiYHanD. Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD. Int J Mech Syst Dyn2024; 4(4): 472–486.
12.
ZhangBLiHKongW, et al. Early-stage fault diagnosis of motor bearing based on kurtosis weighting and fusion of current–vibration signals. Sensors2024; 24(11): 3373.
13.
YangXYangJJinY, et al. A new method for bearing fault diagnosis across machines based on envelope spectrum and conditional metric learning. Sensors2024; 24(9): 2674.
14.
KimYCKoJULeeJ, et al. Latent space alignment based domain adaptation (LSADA) for fault diagnosis of rotating machinery. Adv Eng Inform2024; 62: 102862.
15.
ZhangLFanQLinJ, et al. A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions. Eng Appl Artif Intell2023; 119: 105735.
16.
WangQHuangRXiongJ, et al. A survey on fault diagnosis of rotating machinery based on machine learning. Meas Sci Technol2024; 35(10): 102001.
17.
VashishthaGChauhanSSehriM, et al. A roadmap to fault diagnosis of industrial machines via machine learning: a brief review. Measurement2025; 242: 116216.
18.
ShandhooshVChakrapaniGSugumaranV, et al. Intelligent fault diagnosis for tribo-mechanical systems by machine learning: multi-feature extraction and ensemble voting methods. Knowl Based Syst2024; 305: 112694.
19.
LimISParkJYChoiEJ, et al. Efficient fault diagnosis method of PEMFC thermal management system for various current densities. Int J Hydrogen Energy2021; 46(2): 2543–2554.
20.
WangBQiuWHuX, et al. A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM. Appl Soft Comput2024; 156: 111506.
21.
TangHLiaoZChenP, et al. A robust deep learning network for low-speed machinery fault diagnosis based on multikernel and RPCA. IEEE/ASME Trans Mechatron2022; 27(3): 1522–1532.
22.
HeDWuJJinZ, et al. AGFCN: a bearing fault diagnosis method for high-speed train bogie under complex working conditions. Reliab Eng Syst Saf2025; 258: 110907.
23.
FeisaTTGebremedhenHSKibreteF, et al. One-dimensional deep convolutional neural network-based intelligent fault diagnosis method for bearings under unbalanced health and high-class health states. Struct Control Health Monit2025; 2025(1): 6498371.
24.
MengHGengMHanT. Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis. Reliab Eng Syst Saf2023; 236: 109288.
25.
LvHChenJPanT, et al. Attention mechanism in intelligent fault diagnosis of machinery: a review of technique and application. Measurement2022; 199: 111594.
26.
HeDZhaoJJinZ, et al. DCAGGCN: a novel method for remaining useful life prediction of bearings. Reliab Eng Syst Saf2025; 260: 110978.
27.
ZhaoJHeDJinZ, et al. A new method for bearing remaining useful life prediction based on dynamic wavelet and physical information constraints. Expert Syst Appl2026; 296: 129023.
28.
PatilSSSalunkheVGJadhavPS, et al. A novel bearing faults diagnosis of rotor-bearing systems based on vibration responses and convolutional neural network. Mech Syst Signal Process2025; 236: 113055.
29.
YanSShaoHWangJ, et al. LiConvFormer: a lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention. Expert Syst Appl2024; 237: 121338.
30.
SongBLiuYFangJ, et al. An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples. Neurocomputing2024; 574: 127284.
31.
ZhangHLiYZhangX, et al. Multi-scale group Mamba network with structural attention for rotating machinery fault diagnosis using multisensor data. Adv Eng Inform2025; 67: 103521.
32.
PangBLiuQSunZ, et al. Time-frequency supervised contrastive learning via pseudo-labeling: an unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds. Adv Eng Inform2024; 59: 102304.
33.
ZhaoDShaoDCuiL. CTNet: a data-driven time-frequency technique for wind turbines fault diagnosis under time-varying speeds. ISA Trans2025; 154: 335–351.
34.
WangLXieFZhangX, et al. Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery. Adv Eng Inform2024; 62: 102711.
35.
HeCShiHLiR, et al. Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations. Adv Eng Inform2024; 62: 102568.
36.
UllahNUmarMKimJY, et al. Enhanced fault diagnosis in milling machines using CWT image augmentation and ant colony optimized AlexNet. Sensors2024; 24(23): 7466.
37.
WangSTianJLiangP, et al. Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data. Eng Appl Artif Intell2024; 133: 108146.
38.
WangZXuZCaiC, et al. Rolling bearing fault diagnosis method using time-frequency information integration and multi-scale TransFusion network. Knowl Based Syst2024; 284: 111344.
39.
LiuYChenYLiX, et al. MPNet: a lightweight fault diagnosis network for rotating machinery. Measurement2025; 239: 115498.
40.
TangXXuZWangZ. A novel fault diagnosis method of rolling bearing based on integrated vision transformer model. Sensors2022; 22(10): 3878.
41.
LiuXGohHHXieH, et al. ResGRU: a novel hybrid deep learning model for compound fault diagnosis in photovoltaic arrays considering dust impact. Sensors2025; 25(4): 1035.
42.
DalianYJunjunZHuiL. Capsule networks for intelligent fault diagnosis: a roadmap of recent advancements and challenges. Expert Syst Appl2026; 296: 128814.
43.
RuanDWangJYanJ, et al. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv Eng Inform2023; 55: 101877.
44.
LiQWangYMengL, et al. SDMC-Net: a lightweight and deployable fault diagnosis network using self-distillation and multiscale-depth convolution. IEEE Trans Instrum Meas2025; 74: 1–13.
45.
SmithWARandallRB. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech Syst Signal Process2015; 64–65: 100–131.
46.
HuangQZhouZYangK, et al. TimeBase: the power of minimalism in efficient long-term time series forecasting. In: Proceedings of the 42nd international conference on machine learning, PMLR2025, pp. 26227–26246.
47.
ChenJKaoSHeH, et al. Run, don’t walk: chasing higher FLOPs for faster neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2023, pp. 12021–12031.
48.
LiTZhouZLiS, et al. The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study. Mech Syst Signal Process2022; 168: 108653.
