Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet
Abstract
:1. Introduction
- (1)
- Adaptive adjustment based on the cumulative variance explained ratio in the Principal Component Analysis (PCA) algorithm reduces the data to a uniform dimensionality, addressing the issue of uneven sample lengths. This ensures effective fault diagnosis on input signals of different lengths.
- (2)
- We propose a fault diagnosis method called 1DCNN-SeNet, which leverages 1DCNN to automatically extract local feature information from the data and then utilizes SeNet to adaptively adjust the importance of each channel. This approach significantly enhances diagnostic accuracy and demonstrates distinct advantages.
- (3)
- A comparative analysis between the proposed method and four classical algorithms demonstrates that the proposed method outperforms in adapting to complex fault scenarios in rolling bearing fault diagnosis tasks. The analysis of accuracy curves, loss curves, and confusion matrices confirms the effectiveness and generalization capability of the proposed algorithm.
2. Research Objectives and Methodology Theory
2.1. Research Objectives
2.2. Principal Component Analysis Algorithm
2.3. One-Dimensional Convolutional Neural Network
2.4. Squeeze-and-Excitation Network (SENet)
3. Experiment Validation and Result Analysis
3.1. Description of Experimental Dataset
3.2. Data Normalization Process
3.3. Adaptive Sample Length Selection
3.4. Correlation Analysis of Data after PCA Dimensionality Reduction
3.5. The Adaptive Sample Length-Adjusted 1DCNN-SeNet Diagnostic Model
3.6. Analysis of Experimental Results
3.6.1. Model Training
3.6.2. Model Testing
3.6.3. t-SNE Visualization Analysis
4. Summary
- (1)
- This paper utilizes a PCA algorithm to adaptively adjust the uneven sample lengths, ensuring effective fault diagnosis on samples of different lengths based on the actual length of the input signal.
- (2)
- The 1DCNN network effectively captures the local temporal features of the bearing signal, while the SeNet network adaptsively learns the importance of features from each channel, automatically selecting and enhancing the features most helpful for bearing fault diagnosis. The combination of the two enables a more comprehensive capture of fault features in the bearing signal, thereby improving the accuracy and generalization capability of fault diagnosis.
- (3)
- Comparative analysis with four classical fault diagnosis algorithms demonstrates the significant advantages of the proposed model in bearing fault diagnosis tasks. It exhibits better adaptability to complex fault scenarios and different datasets, thus proving the effectiveness and superiority of the proposed algorithm.
- (4)
- Based on the research conducted in this paper, future endeavors could involve expanding the dataset to include a wider range of fault types and larger-scale real bearing fault data. This expansion would facilitate more comprehensive fault diagnosis and allow for deeper performance evaluation and comparative analysis of model algorithms. Additionally, it would be beneficial to seek engineering practical cases and apply the established models in real-world scenarios to validate their effectiveness.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data States | Data Symbols | Fault Diameter/Inches | Number of Samples/Units | Sample Length | Labels |
---|---|---|---|---|---|
Normal | Normal_1 | —— | 1000 | 1024 | 0 |
Roller Fault | B007_1 | 0.007 | 1000 | 1024 | 1 |
Roller Fault | B014_1 | 0.014 | 1000 | 1024 | 2 |
Inner Race Fault | IR007_1 | 0.007 | 1000 | 1024 | 3 |
Inner Race Fault | IR014_1 | 0.014 | 1000 | 1024 | 4 |
Outer Race Fault | OR007@6_1 | 0.007 | 1000 | 1024 | 5 |
Outer Race Fault | OR014@6_1 | 0.014 | 1000 | 1024 | 6 |
Roller Fault | B021_1 | 0.021 | 1000 | 1004 | 7 |
Inner Race Fault | IR021_1 | 0.021 | 1000 | 1014 | 8 |
Outer Race Fault | OR021@6_1 | 0.021 | 1000 | 1034 | 9 |
Network Layer | Number of Convolutional Kernels and Number of Neurons | Convolutional Kernel Size | Step | Output Size |
---|---|---|---|---|
Convolutional layer 1 | 32 | 1 × 3 | 1 × 1 | 32 × 489 |
Pooling layer 1 | 32 | 1 × 2 | 1 × 2 | 32 × 244 |
Convolutional layer 2 | 64 | 1 × 3 | 1 × 1 | 64 × 244 |
Pooling layer 2 | 64 | 1 × 2 | 1 × 2 | 64 × 122 |
Convolutional layer 3 | 128 | 1 × 3 | 1 × 1 | 128 ×122 |
Pooling layer 3 | 128 | 1 × 2 | 1 × 2 | 128 × 61 |
SeNet layer | - | - | - | 128 × 61 |
Fully connected layer1 | 1000 | - | - | 1000 × 1 |
Fully connected layer 2 | 100 | - | - | 100 ×1 |
Output layer | 10 | - | - | 10 × 1 |
Method | Average Accuracy % | Standard Deviation |
---|---|---|
CNN | 99.05 | 0.015 |
DNN | 98.95 | 0.12 |
RNN | 97.65 | 0.26 |
ResNet18 | 97.40 | 0.135 |
1DCNN-SeNet | 99.30 | 0.009 |
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Xu, F.; Sui, Z.; Ye, J.; Xu, J. Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet. Processes 2024, 12, 702. https://doi.org/10.3390/pr12040702
Xu F, Sui Z, Ye J, Xu J. Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet. Processes. 2024; 12(4):702. https://doi.org/10.3390/pr12040702
Chicago/Turabian StyleXu, Feng, Zhen Sui, Jiangang Ye, and Jianliang Xu. 2024. "Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet" Processes 12, no. 4: 702. https://doi.org/10.3390/pr12040702