Dual-Channel Parallel Multimodal Feature Fusion for Bearing Fault Diagnosis
Abstract
1. Introduction
- 1.
- A dual-channel parallel-architecture-based multi-modal feature fusion method is proposed for bearing fault diagnosis. This framework simultaneously processes raw one-dimensional vibration signals and two-dimensional time–frequency representations through dedicated channels to extract complementary fault features. The parallel integration of heterogeneous modal data enhances feature complementarity and significantly improves the model’s generalization capability.
- 2.
- A channel attention mechanism is incorporated to recalibrate feature channel weights, maximizing utilization of extracted sample features and enhancing the model’s focus on discriminative characteristics.
- 3.
- Comprehensive experimental validation was conducted across multiple benchmark datasets, including comparative analysis, noise robustness tests, and ablation studies. These experiments conclusively demonstrate the validity and efficacy of the proposed dual-channel parallel multi-modal feature fusion method for bearing fault diagnosis, confirming its superiority in this domain.
2. Theoretical Background
2.1. CWT
2.2. CNN
2.3. BiGRU
2.4. Channel Attention
3. Model Construction
3.1. Data Preprocessing
3.2. Model Construction
3.2.1. Model Construction
3.2.2. Diagnostic Workflow
4. Experimental Results
4.1. Dataset
- (1)
- CWRU: In order to assess the effectiveness of the suggested approach for diagnosing faults in rolling bearings, experiments were conducted using the bearing dataset from Case Western Reserve University (CWRU). The CWRU bearing fault dataset is widely adopted in fault diagnosis and prognosis research, containing vibration data under various fault modes and operating conditions. The experimental platform comprises a three-phase induction motor, torque transducer, dynamometer, and SKF6205 drive-end bearings.
- (2)
- SEU: The Southeast University (SEU) test rig was equipped with an electric motor, a motor controller, a speed reducer, a planetary gearbox, a brake, and a brake controller. This study utilized a bearing vibration dataset acquired under the operating condition of a 30 Hz (1800 rpm) rotational speed and a 2 V (7.32 Nm) load, with a sampling frequency of 5120 Hz. This dataset encompasses data representing five distinct states: ball fault, inner ring fault, outer ring fault, compound fault, and healthy working state. Specifications for the SEU fault categories are detailed in Table 3.
4.2. Comparative Methods and Experimental Results
4.2.1. Comparative Methods
4.2.2. Case 1
4.2.3. Case 2
4.3. Ablation Experiments
5. Conclusions and Future Work
5.1. Conclusions
- 1.
- The proposed dual-channel parallel multi-modal feature fusion model converts the input data into time–frequency images and employs dilated convolution for feature extraction. This approach capitalizes on the strong capability of two-dimensional dilated convolution in capturing image features, where dilated convolution expands the receptive field without reducing spatial resolution, thereby preserving fine-grained image details effectively. In processing one-dimensional signals, a one-dimensional convolutional neural network (1DCNN) is employed for effective local feature identification, succeeded by a bidirectional gated recurrent unit (BiGRU) to analyze intricate time-based relationships within the sequence. The parallel dual-channel architecture effectively exploits both temporal and spatial characteristics of the data, significantly enriching feature representation and achieving high diagnostic accuracy while maintaining strong generalization ability.
- 2.
- The incorporation of a channel attention mechanism after the feature fusion layer enhances the model’s ability to focus on critical fault-related features. By adaptively reweighting channel-wise features, the attention mechanism suppresses interference from redundant or irrelevant features, thereby further improving diagnostic accuracy.
5.2. Future Work
- 1.
- Real-World Data Collection and Validation. We plan to establish collaborations with industrial partners to collect vibration data from operational machinery in real industrial environments. This will include data from various types of rotating equipment operating under diverse working conditions, with natural fault progression being captured rather than artificially induced defects.
- 2.
- Variable Operating Condition Adaptation. A major focus will be on developing adaptive mechanisms that can handle variable operating conditions. We intend to investigate domain adaptation techniques and transfer learning approaches to enhance the model’s robustness when faced with changing rotational speeds, varying loads, and different operational regimes.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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One-Dimensional Feature Extraction | Two-Dimensional Feature Extraction | |
---|---|---|
Input Data | One-dimensional data (1024, 1) | Two-dimensional data (64, 64, 3) |
Feature extraction layer | Conv1D (Filters: 8, Kernel size: , Relu) | Conv2D (filters: 32; kernel size: , Dilation = 1, Relu) |
MaxPool1D (kernel size: 2; stride: 2) | ||
Conv1D (filters: 16; kernel size: , Relu) | Conv2D (filters: 64; kernel size: ; dilation = 2, Relu) | |
MaxPool1D (kernel size: 2; stride: 2) | ||
Dropout | Conv2D (filters: 64; kernel size: ; dilation = 4, Relu) | |
Conv1D (filters: 8; kernel size: ; Relu) | ||
Dropout | ||
Bidirectional (GRU(32)) | ||
Dropout | ||
Fusion layer | Feature fusion layer | |
Attention mechanism | Channel attention mechanism | |
Dense (128, Relu) | ||
Fully connected layer | Dense (fault categories, softmax) |
Fault Type | Diameter\mm | Motor Load\HP | Label | Size of the Training Set | Size of the Valid Set |
---|---|---|---|---|---|
B007 | 0.1778 | 1 | 0 | 120 | Data |
B014 | 0.3556 | 1 | 1 | 120 | 40 |
B021 | 0.5334 | 1 | 2 | 120 | 40 |
IR007 | 0.1778 | 1 | 3 | 120 | 40 |
IR014 | 0.3556 | 1 | 4 | 120 | 40 |
IR021 | 0.5334 | 1 | 5 | 120 | 40 |
OR007 | 0.1778 | 1 | 6 | 120 | 40 |
OR014 | 0.3556 | 1 | 7 | 120 | 40 |
OR021 | 0.5334 | 1 | 8 | 120 | 40 |
Normal | / | / | 9 | 120 | 40 |
Fault Type | Label | Diameter\mm | Working Condition | Size of the Training Set | Size of the Valid Set |
---|---|---|---|---|---|
Ball fault | 0 | 0.508 | ball_30_2.csv | 180 | 60 |
Compound fault | 1 | 0.508 | comb_30_2.csv | 180 | 60 |
Health | 2 | / | health_30_2.csv | 180 | 60 |
Inner ring fault | 3 | 0.508 | inner_30_2.csv | 180 | 60 |
Outer ring fault | 4 | 0.508 | outer_30_2.csv | 180 | 60 |
MLP | GRU | LSTM | CNN | Proposed Method | |
---|---|---|---|---|---|
−6 db | 64.50% (0.56%) | 77.50% (1.03%) | 75.25% (1.41%) | 76.00% (1.03%) | 87.00% (0.64%) |
59.67% (1.17%) | 74.93% (0.90%) | 74.33% (0.87%) | 72.67% (0.75%) | 85.87% (0.58%) | |
−4 db | 65.25% (0.79%) | 78.00% (0.91%) | 77.00% (0.67%) | 77.50% (0.61%) | 87.75% (0.56%) |
61.67% (0.96%) | 76.93% (0.87%) | 75.00% (0.75%) | 73.27% (0.58%) | 86.73% (0.48%) | |
0 db | 78.25% (0.50%) | 83.50% (0.48%) | 80.00% (0.32%) | 81.50% (0.43%) | 92.50% (0.43%) |
70.00% (0.87%) | 82.07% (0.75%) | 78.33% (0.63%) | 78.13% (0.43%) | 91.67% (0.27%) | |
4 db | 80.00% (0.62%) | 86.25% (0.62%) | 84.50% (0.48%) | 84.50% (0.50%) | 94.50% (0.48%) |
76.00% (0.90%) | 83.27% (0.87%) | 82.47% (0.64%) | 81.47% (0.59%) | 93.27% (0.38%) | |
6 db | 84.25% (0.64%) | 87.75% (0.67%) | 85.50% (0.57%) | 85.75% (0.57%) | 95.2% (0.48%) |
77.33% (0.90%) | 84.47% (0.90%) | 83.93% (0.75%) | 82.73% (0.52%) | 94.67% (0.38%) |
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Li, W.; Cai, H.; Yang, X.; Xue, Y.; Ye, J.; Hu, X. Dual-Channel Parallel Multimodal Feature Fusion for Bearing Fault Diagnosis. Machines 2025, 13, 950. https://doi.org/10.3390/machines13100950
Li W, Cai H, Yang X, Xue Y, Ye J, Hu X. Dual-Channel Parallel Multimodal Feature Fusion for Bearing Fault Diagnosis. Machines. 2025; 13(10):950. https://doi.org/10.3390/machines13100950
Chicago/Turabian StyleLi, Wanrong, Haichao Cai, Xiaokang Yang, Yujun Xue, Jun Ye, and Xiangyi Hu. 2025. "Dual-Channel Parallel Multimodal Feature Fusion for Bearing Fault Diagnosis" Machines 13, no. 10: 950. https://doi.org/10.3390/machines13100950
APA StyleLi, W., Cai, H., Yang, X., Xue, Y., Ye, J., & Hu, X. (2025). Dual-Channel Parallel Multimodal Feature Fusion for Bearing Fault Diagnosis. Machines, 13(10), 950. https://doi.org/10.3390/machines13100950