In large mechanical bearings such as water turbine generators, fault diagnosis is often carried out at the edge end due to the risk of data transmission. The edge-end diagnosis model faces the challenge of cross-domain diagnosis ability posed by diverse operating conditions, but the limited computing power of edge devices is difficult to support the computational overhead of conventional cross-domain models. To address these challenges, a lightweight domain generalization model (LDGM) for edge deployment is proposed. The main innovation lies in the Mamba-MP architecture, which effectively decouples the global pattern and local transient influence features, and maximizes feature extraction while ensuring model lightweight. In addition, in order to enhance the domain robustness, the data augmentation alignment training strategy is introduced. This strategy exploits the decoupling property of LDGM and applies the cooperative perturbation to the global time–frequency information and instantaneous features to enhance the prediction consistency in order to improve the generalization ability under different working conditions. Experimental results on 14 cross-condition transfer tasks show that the average diagnostic accuracy of LDGM under different transfer tasks is more than 99%, which is significantly better than the comparison baseline.
ZhiSNiuYMaL, et al. Local entropy selection scaling-extracting Chirplet transform for enhanced time-frequency analysis and precise state estimation in reliability-focused fault diagnosis of non-stationary signals. Eksploatacja Niezawodność–Maintenance Reliab2025; 28: 205977.
2.
QianQZhangJLuoJ, et al. Integrated-dispersion manifold distance: a new distribution discrepancy metric for machine fault transfer diagnosis under time-varying conditions. IEEE Trans Cybernet. Epub ahead of print 17November2025. DOI: 10.1109/TCYB.2025.3630879.
3.
LiXRanGXiaoS, et al. A fault diagnosis method for bearings based on multiscale graph convolutional network under non-stationary speed conditions. Mechan Mach Theory2025; 217: 106267.
4.
LiXLiGWangT, et al. Knowledge extraction and retrieval-augmented generation for intelligent maintenance of wind power equipment based on graph attention networks. Chin J Mech Eng2025; 2025: 100141.
5.
ChenCWangTMaoD, et al. A multi-scale graph pyramid attention network with knowledge distillation towards edge computing robotic fault diagnosis. Expert Syst Appl2025; 260: 125469.
6.
GeLZhaoDWangT, et al. High-dimensional optimized extraction Chirplet transform: algorithm and applications. ISA Trans2026; 169: 520–534.
7.
ShiJZhongJZhangY, et al. A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction. Reliab Eng Syst Safe2024; 243: 109821.
8.
WangCTianBYangJ, et al. Neural-transformer: a brain-inspired lightweight mechanical fault diagnosis method under noise. Reliab Eng Syst Safe2024; 251: 110409.
9.
ZhangXSunJWangJ, et al. PAOLTransformer: pruning-adaptive optimal lightweight Transformer model for aero-engine remaining useful life prediction. Reliab Eng Syst Safe2023; 240: 109605.
10.
LiXWuXYuJ, et al. A fault diagnosis data augmentation method integrating multimodal non-Gaussian denoising diffusion generative adversarial network. Adv Eng Inform2025; 68: 103776.
11.
WangSXuQZhangK, et al. Selection refines diagnosis: Mamba for acoustic weak fault diagnosis combining feature mode decomposition and selection. Adv Eng Inform2025; 66: 103421.
12.
HeYShenW.FedITA: a cloud–edge collaboration framework for domain generalization-based federated fault diagnosis of machine-level industrial motors. Adv Eng Inform2024; 62: 102853.
13.
ZhaoPZhangWCaoX, et al. Denoising diffusion probabilistic model-enabled data augmentation method for intelligent machine fault diagnosis. Eng Appl Artif Intell2025; 139: 109520.
14.
LiXXiaoSLiQ, et al. The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy. Reliab Eng Syst Safe2025; 261: 111122.
15.
LiuBYanCHeC, et al. An interpretable physics-informed subdomain moment-enhanced adaptation network for unsupervised transfer fault diagnosis of rolling bearing. Adv Eng Inform2025; 67: 103491.
16.
LiuBYanCLiuY, et al. ISEANet: an interpretable subdomain enhanced adaptive network for unsupervised cross-domain fault diagnosis of rolling bearing. Adv Eng Inform2024; 62: 102610.
17.
YeZWuJHeX, et al. A gradient alignment federated domain generalization framework for rotating machinery fault diagnosis. IEEE Intern Thing J2025; 12: 24764–24774.
18.
WangJRenHShenC, et al. Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis. Reliab Eng Syst Safe2024; 243: 109879.
19.
WangGLiuDXiangJ, et al. Attention guided partial domain adaptation for interpretable transfer diagnosis of rotating machinery. Adv Eng Inform2024; 62: 102708.
20.
LiuYTianYZhaoY, et al. Vmamba: visual state space model. Adv Neural inform Process Syst2024; 37: 103031–103063.
21.
MaXZhangXPunM-O.Rs 3 mamba: visual state space model for remote sensing image semantic segmentation. IEEE Geosci Remote Sens Lett2024; 21: 6011405.
22.
WangPSongYWangX, et al. MD-BiMamba: an aero-engine inter-shaft bearing fault diagnosis method based on Mamba with modal decomposition and bidirectional features fusion strategy. Measurement2025; 242: 115870.
23.
DaoTGuA.Transformers are SSMs: generalized models and efficient algorithms through structured state space duality. arXiv preprint. arXiv: 2405.21060, 2024.
24.
LiQLiHHuW, et al. Transparent operator network: a fully interpretable network incorporating learnable wavelet operator for intelligent fault diagnosis. IEEE Trans Industr Inform2024; 20: 8628–8638.
25.
LinHHuangXChenZ, et al. Matching pursuit network: an interpretable sparse time–frequency representation method toward mechanical fault diagnosis. IEEE Trans Neural Netw Learn Syst2024; 36: 12388.
26.
GargPKeprateASoniG, et al. Fault detection in wind turbine bearings by coupling knowledge graph and machine learning approach. J Dyn Monitor Diagnost2025; 4(4): 250–263.
27.
ChenJLiTHeJ, et al. An interpretable wavelet Kolmogorov–Arnold convolutional LSTM for spatial-temporal feature extraction and intelligent fault diagnosis. J Dyn Monitor Diagnost2025; 4(3): 183–193.
28.
GuAGoelKRéC.Efficiently modeling long sequences with structured state spaces. arXiv preprint. arXiv:2111.00396, 2021.
29.
ZhiSSuKYuJ, et al. An unsupervised transfer learning bearing fault diagnosis method based on multi-channel calibrated Transformer with shiftable window. Struc Health Monitor. Epub ahead of print 15March2025. DOI: 10.1177/14759217251324671.
30.
QiJChenZKongY, et al. Attention-guided graph isomorphism learning: a multi-task framework for fault diagnosis and remaining useful life prediction. Reliab Eng Syst Safe2025; 263: 111209.
31.
MinghuiHYaHXinzhiL, et al. Digital twin model of gas turbine and its application in warning of performance fault. Chin J Aeronaut2023; 36(3): 449–470.
32.
ZhuangJWuJPeiG, et al. A neuro-fuzzy approach with hypergraph convolution for fault diagnosis in industrial devices. J Reliab Sci Eng2025; 1(3): 035301.
33.
SpirtoMNicolellaAMellusoF, et al. Enhancing SDP-CNN for gear fault detection under variable working conditions via multi-order tracking filtering. J Dyn Monitor Diagnost2025; 4(4): 226–238.
34.
HuangXBelongieS.Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE international conference on computer vision (ICCV), Venice, 22–29 October 2017, pp.1501–1510. New York: IEEE.
35.
ZhouKYangYQiaoY, et al. Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008, 2021.
36.
ShaoSMcAleerSYanR, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Industr Inform2018; 15(4): 2446–2455.
37.
LiKPingXWangH, et al. Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis. Sensors2013; 13(6): 8013–8041.
38.
ChenLLiQShenC, et al. Adversarial domain-invariant generalization: a generic domain-regressive framework for bearing fault diagnosis under unseen conditions. IEEE Trans Industr Inform2021; 18(3): 1790–1800.
