Data-driven fault diagnosis methods have made substantial advancements in bearing prediction and health management. However, the scarcity of fault samples hinders their application in engineering practice. To tackle this challenge, we put forward a novel knowledge and data collaboration-driven method with modified stacked broad autoencoder (MSBAE) for few-shot bearing fault diagnosis. First, 30 domain-knowledge-based fault features are derived from the time, frequency, and time-frequency domains. Then, by introducing the maximum mean discrepancy and manifold regularization into the original stacked broad autoencoder, MSBAE is constructed to integrate the prior knowledge into its self-learning process, thereby mining more discriminative and robust features from massive unlabeled data. Finally, a least squares classification layer is employed on top of MSBAE for fault recognition, and the structure parameters of MSBAE are fine-tuned with limited labeled fault samples. Extensive experiments on three bearing fault datasets confirm that our method exceeds other cutting-edge methods, particularly under small-sample conditions.
ZhuDLiuGWuX, et al. An enhanced empirical Fourier decomposition method for bearing fault diagnosis. Struct Health Monit2024; 23: 903–923.
2.
YangXWangYHuangY, et al. A uniform phase decoupled iterative filtering and multiscale sliding fractal box dimension method for rolling bearing fault diagnosis. Struct Health Monit Epub ahead of print 16 August 2025. DOI: 10.1177/14759217251358710.
3.
MaQLiZYangK, et al. Hybrid attention-driven transfer learning with DSCNN for cross-domain bearing fault diagnosis under variable operating conditions. Struct Durab Health Monit2025; 19: 1607.
4.
WuYBaiYYangS, et al. Extracting random forest features with improved adaptive particle swarm optimization for industrial robot fault diagnosis. Measurement2024; 229: 114451.
5.
LiYXuMZhaoH, et al. Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis. Mech Mach Theory2016; 98: 114–132.
6.
HeZShaoHChengJ, et al. Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals. Measurement2020; 163: 107965.
7.
GuanYMengZSunD, et al. Rolling bearing fault diagnosis based on information fusion and parallel lightweight convolutional network. J Manuf Syst2022; 65: 811–821.
8.
ZhangLQiuJLiuJ, et al. CTNet: an interpretable convolutional neural network for bearing fault diagnosis. Struct Health Monit Epub ahead of print 7 October 2025. DOI: 10.1177/14759217251382151.
9.
ChenHWangX-BYangZ-X, et al. Privacy-preserving intelligent fault diagnostics for wind turbine clusters using federated stacked capsule autoencoder. Expert Syst Appl2024; 254: 124256.
10.
WangLRaoHDongZ, et al. Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder. Struct Health Monit2024; 23: 3084–3104.
11.
JiangYZhengLTangC, et al. LSTM-based node-gated graph neural network for cross-condition few-shot bearing fault diagnosis. IEEE Sens J2024; 24: 3445–3456.
12.
HouYWangJChenZ, et al. Diagnosisformer: an efficient rolling bearing fault diagnosis method based on improved Transformer. Eng Appl Artif Intell2023; 124: 106507.
13.
LiaoXWangDQiuS, et al. SLDAE: an interpretable stacked denoising auto-encoders for fan fault diagnosis on steelmaking workshops. Adv Eng Inform2025; 65: 103260.
14.
ZengWYuLXuF, et al. Wear indicator construction for rolling bearings based on an enhanced and unsupervised stacked auto-encoder. Soft Comput2024; 28: 8835–8848.
15.
ShaoHXiaMWanJ, et al. Modified stacked autoencoder using adaptive Morlet wavelet for intelligent fault diagnosis of rotating machinery. IEEE/ASME Trans Mechatronics2021; 27: 24–33.
16.
LiuSHeJChenZ, et al. Discriminative stacked auto-encoder: feature-integration boosting for bearing fault diagnosis. IEEE Sens J2023; 23: 27549–27558.
17.
ChenLMaYHuH, et al. An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment. Measurement2023; 214: 112774.
18.
LiuLZhengYLiangS. Variable-wise stacked temporal autoencoder for intelligent fault diagnosis of industrial systems. IEEE Trans Ind Inform2024; 20: 7545–7555.
19.
FuYCaoHChenX, et al. Broad auto-encoder for machinery intelligent fault diagnosis with incremental fault samples and fault modes. Mech Syst Signal Process2022; 178: 109353.
20.
DengNPanHLiuH, et al. GBLN: a new generative broad learning network for rolling bearing fault diagnosis. Nondestr Test Eval Epub ahead of print 19 June 2025. DOI: 10.1080/10589759.2025.2521359.
21.
YuZZhongZYangK, et al. Broad learning autoencoder with graph structure for data clustering. IEEE Trans Knowl Data Eng2023; 36: 49–61.
22.
YangKShiYYuZ, et al. Stacked one-class broad learning system for intrusion detection in industry 4.0. IEEE Trans Ind Inform2022; 19: 251–260.
23.
PeiZJiangHLiX, et al. Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning. Meas Sci Technol2021; 32: 084007.
24.
LuoSHuangXWangY, et al. Transfer learning based on improved stacked autoencoder for bearing fault diagnosis. Knowl Based Syst2022; 256: 109846.
25.
HanBWangXJiS, et al. Data-enhanced stacked autoencoders for insufficient fault classification of machinery and its understanding via visualization. IEEE Access2020; 8: 67790–67798.
26.
SunMWangHLiuP, et al. Stack autoencoder transfer learning algorithm for bearing fault diagnosis based on class separation and domain fusion. IEEE Trans Ind Electron2021; 69: 3047–3058.
27.
PangS. Stacked maximum independence autoencoders: a domain generalization approach for fault diagnosis under various working conditions. Mech Syst Signal Process2024; 208: 111035.
28.
Von RuedenLMayerSBeckhK, et al. Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans Knowl Data Eng2021; 35: 614–633.
29.
LeeJDLeiQSaunshiN, et al. Predicting what you already know helps: provable self-supervised learning. Adv Neural Inform Process Syst2021; 34: 309–323.
30.
SuYShiLZhouK, et al. Knowledge-informed deep networks for robust fault diagnosis of rolling bearings. Reliab Eng Syst Saf2024; 244: 109863.
31.
YinTLuNGuoG, et al. Knowledge and data dual-driven transfer network for industrial robot fault diagnosis. Mech Syst Signal Process2023; 182: 109597.
32.
LiuRDingXShaoY. Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions. Adv Eng Inform2024; 61: 102493.
33.
MiaoZXiaYZhouF, et al. Fault diagnosis of wheeled robot based on prior knowledge and spatial-temporal difference graph convolutional network. IEEE Trans Ind Inform2022; 19: 7055–7065.
34.
JiangGWangJWangL, et al. An interpretable convolutional neural network with multi-wavelet kernel fusion for intelligent fault diagnosis. J Manuf Syst2023; 70: 18–30.
35.
GongXZhangTChenCP, et al. Research review for broad learning system: algorithms, theory, and applications. IEEE Trans Cybern2021; 52: 8922–8950.
36.
LiXChenHWeiD, et al. Hypergraph convolution rebalancing cascade broad learning for coal-rock cutting state recognition. IEEE Sens J2024; 24: 41753–41766.
37.
LiXYangYHuN, et al. Maximum margin Riemannian manifold-based hyperdisk for fault diagnosis of roller bearing with multi-channel fusion covariance matrix. Adv Eng Inform2022; 51: 101513.
38.
LiXZhongXShaoH, et al. Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression. Reliab Eng Syst Saf2021; 216: 108018.
39.
YanXJiaM. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing2018; 313: 47–64.
40.
FanMGuNQiaoH, et al. Dimensionality reduction: an interpretation from manifold regularization perspective. Inform Sci2014; 277: 694–714.
41.
JiaXZhaoMDiY, et al. Assessment of data suitability for machine prognosis using maximum mean discrepancy. IEEE Trans Ind Electron2017; 65: 5872–5881.
42.
WangJZhangXZhangZ, et al. Attention guided multi-wavelet adversarial network for cross domain fault diagnosis. Knowl Based Syst2024; 284: 111285.
43.
DagaAPFasanaAMarchesielloS, et al. The Politecnico di Torino rolling bearing test rig: description and analysis of open access data. Mech Syst Signal Process2019; 120: 252–273.
44.
WangXChenHZhaoJ, et al. Wind turbine fault diagnosis for class-imbalance and small-size data based on stacked capsule autoencoder. IEEE Trans Ind Inform2024; 20: 12694–12704.
45.
YangDKarimiHRSunK. Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw2021; 141: 133–144.
46.
HouZ-GWangH-WLvS-L, et al. Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition. Meas Sci Technol2022; 34: 035109.
47.
WangZDingYHanT, et al. Adaptive attention-driven few-shot learning for robust fault diagnosis. IEEE Sens J2024; 24: 26034–26043.
48.
ShaoHMingYLiuY, et al. Small sample gearbox fault diagnosis based on improved deep forest in noisy environments. Nondestr Test Eval2025; 40: 3935–3956.
49.
LiXXingZXiangL, et al. Memory-augmented prototypical meta-learning method for bearing fault identification under few-sample conditions. Neurocomputing2025; 635: 129996.
50.
ZhaoYShengTLiD. Data augmentation fault diagnosis of rolling machinery using condition denoising diffusion probabilistic model and improved CNN. IEEE Trans Instrum Meas2025; 74: 1–12.
