2026· Engineering Research Express· Vol 8, pp. 165213· 0 citations· 37 references
Physics
TL;DR
A dual-channel attention and feature selection-based fault diagnosis method for rolling bearings is proposed, and a corresponding digital twin-based interaction system is further developed.
Abstract
Rolling bearings are critical to the safe and reliable operation of rotating machinery and to predictive maintenance. However, existing fault diagnosis methods still face challenges in insufficient feature representation, inadequate heterogeneous information fusion, and limited diagnostic accuracy and stability. To address these issues, a dual-channel attention and feature selection-based fault diagnosis method for rolling bearings is proposed, and a corresponding digital twin-based interaction system is further developed. First, a hierarchical bearing digital twin architecture composed of the physical layer, data layer, model layer, function layer, and application layer is established. At the model layer, a signal channel based on multi-scale sample entropy and bidirectional long short-term memory (BILSTM) is designed to extract discriminative features from raw vibration signals, while an image channel based on Gramian angular difference field, convolutional neural network, and BILSTM is constructed to learn complementary spatial representations from two-dimensional signal images. Subsequently, multi-head self-attention (MHSA) is introduced to adaptively weight and fuse heterogeneous dual-channel features, and a Pearson-correlation-based threshold filtering strategy is employed to remove highly redundant dimensions before fault prediction. Based on the Case Western Reserve University bearing fault dataset, the performance of the proposed model was comprehensively evaluated through model training, t-SNE visualization analysis, threshold parameter sensitivity analysis, feature selection analysis under different operating conditions, comparison with conventional models, and ablation study analysis. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 99.05% across four working conditions. Compared with the best-performing baseline method, the proposed model improves the average diagnostic accuracy by 0.8%. In addition, ablation studies show that removing the MHSA module, the Pearson-correlation-based screening module, and both modules together reduces the average accuracy to 98.61%, 97.99%, and 97.76%, respectively, further confirming the effectiveness of the proposed components.
To address the issues of low accuracy and insufficient generalization capabilities in traditional methods for diagnosing bearing faults under variable operating conditions, we propose a vision-temporal bimodal multi-channel feature fusion method for rolling bearing fault diagnosis based on domain generalization (DG). This approach constructs a parallel architecture for extracting bimodal features: on one hand, multiple signal processing techniques are employed to transform raw vibration signals into multi-perspective two-dimensional visual feature maps as visual modality input, while simultaneously employing variational modal decomposition to decompose vibration signals into a series of eigenmode functions constituting the temporal modality input. At the model level, a multi-channel large-kernel convolutional network and a global attention-enhanced bidirectional gated recurrent unit network are designed to extract deep features from the visual and temporal modalities, respectively. Subsequently, feature vectors from each channel are concatenated in the feature dimension, with fault classification performed via a progressive dimensionality reduction classifier. Experiments conducted using bearing datasets from case western reserve university and the University of Paderborn in Germany demonstrate that this method can diagnose bearing failures under cross-conditions—even when trained solely on source-domain data and without exposure to target-domain data during training—and that its DG accuracy outperforms that of existing mainstream advanced methods.
Yu-Han Liu, Yong-Fang Yao, Juan Ren et al.· Engineering Research Express· 0 citations
An ensemble attention-based residual convolutional neural network optimized by the vortex search algorithm can effectively overcome the limitations of individual models and achieve superior fault identification performance than existing methods under many types of severe conditions.
This smart fault diagnosis method based on the Time Convolution Network - Bidirectional Gated Recurrent Unit - Attention Model (TCN-BiGRU-Attention) can achieve high-precision and stable fault diagnosis for rolling bearings, providing an effective intelligent diagnosis solution for engineering applications.
Ming-Li Li, Zhu Yuan· Frontiers of Mechanical Engi...· 0 citations
A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (MDSC) and a convolutional neural network–Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continuous wavelet transform (CWT) converts one-dimensional vibration signals into two-dimensional time-frequency images to enhance fault representation. Then, multi-scale convolution (MSC) and depthwise separable convolution (DSC) are introduced to extract local impulsive features and fault patterns at different scales with fewer parameters. A CNN-Transformer architecture is further developed, where convolutional neural network (CNN) captures local details and Transformer models global dependencies. In addition, pretraining-finetuning, data augmentation, label smoothing, and normal sample optimization are adopted to improve training stability and diagnostic performance. Experimental results show accuracies of 98.80% on the Xi’an Jiaotong University bearing dataset (XJTU-SY) and 100.00% on the Case Western Reserve University bearing dataset (CWRU), demonstrating strong discriminative ability, stability, and robustness.
Shuai Yang, Yanchao Chen, Yang Yu· Engineering Research Express· 0 citations
To tackle the persistent challenges of low bearing fault diagnosis accuracy—specifically the difficulty of extracting faint fault features under complex, variable operating conditions and strong background noise, as well as the tendency to lose deep temporal dependencies—this paper proposes a novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network. First, to overcome the heuristic parameter selection and frequency-band aliasing inherent in traditional Variational Mode Decomposition (VMD), the Fast Fourier Transform (FFT) is introduced. By extracting global spectral prior information, the FFT guides the VMD to perform adaptive decomposition and effective denoising of non-stationary vibration signals. Subsequently, a dual-path Bi-TCN-Bi-GRU diagnostic model is constructed. The dilated causal convolution mechanism of the Bidirectional Temporal Convolutional Network (Bi-TCN) is utilized to extract deep local spatial features, while the Bidirectional Gated Recurrent Unit (Bi-GRU) is integrated to deeply mine the forward and backward dynamic evolutionary dependencies within the sequence. Experimental results demonstrate that even under severe background noise with a signal-to-noise ratio (SNR) of -2 dB, the proposed method maintains an exceptional diagnostic accuracy of 98.21% on a self-built bearing dataset and 99.90% on the Southeast University (SEU) dataset. This study provides a highly robust and promising solution for enhancing bearing safety and predictive maintenance in complex industrial environments.
The non-stationarity of high-speed train axle-box bearing vibration signals, combined with distribution discrepancies between laboratory and field data, makes it challenging to directly apply fault diagnosis methods trained on experimental data to actual operating conditions. This paper proposes a four-branch multi-modal unsupervised domain-adaptive fault diagnosis framework, termed CRG-DA Net, based on ConvNeXt and ResNet1D, aimed at enabling cross-condition fault diagnosis under unlabeled target data. Multi-scale and multi-domain fault features are extracted from raw vibration signals using envelope spectrum analysis, short-time Fourier transform, and wavelet transform. A four-branch parallel network is then constructed, employing ConvNeXt-Tiny for modeling the time-frequency representations and ResNet1D for learning the time-domain characteristics of raw signals, with each branch generating high-dimensional feature representations and corresponding fault prediction logits. To account for the varying discriminative contributions of different modalities, a gated dynamic fusion mechanism is introduced, which computes sample-specific fusion weights from the concatenated branch features and integrates the individual branch predictions into a final fused output. In addition, adversarial domain adaptation combined with pseudo-label self-training is employed to align the source and target domain feature distributions, while target samples are classified following the same gated fusion and prediction procedure. Extensive experiments on multi-condition laboratory datasets and real-world operating data demonstrate that the proposed CRG-DA Net achieves outstanding fault diagnosis performance, meeting the expected experimental performance and exhibiting strong generalization across different operating conditions.
Zhihao Zhao, Li Xu, Jingjing Cai et al.· Measurement and control (Lon...· 1 citation