Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM
Abstract
:1. Introduction
2. Fault Diagnosis for Distribution Box
2.1. Distribution Box in Rolling Mill
2.2. Installation of Sensors
2.3. Intelligent Fault Diagnosis for Distribution Box
3. Framework of Proposed Model
3.1. Spatial Feature Extraction Based on Convolutional Neural Networks
3.2. Temporal Feature Extraction Based on Long Short-Term Memory Network
3.3. Fault Diagnosis Model Based on Spatiotemporal Feature Extraction
4. Model Performance Analysis of Fault Diagnosis
4.1. Dataset Introduction
4.2. Model Parameters and Evaluation Metrics
4.3. Model Comparison
4.4. Verification of Noise Robustness
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Specifications | Parameters |
---|---|
Production line name | Hot rolling 1580 |
Capacity | 3 million tons per year |
Thickness range | 1.2 mm to 12.5 mm |
Maximum width | 1580 mm |
Production speed | 5 m/s to 15 m/s |
On year | Since 2000 |
Number of stands | 7 finishing mills |
Fault Location | Label | Sample Number | Training/Testing |
---|---|---|---|
Pitting of tooth flanks | A | 322 | 225/97 |
Flat-headed sleeve tooth crack | B | 322 | 225/97 |
Gear surface crack | C | 322 | 225/97 |
Gear tooth surface spalling | D | 322 | 225/97 |
Normal conditions | E | 322 | 225/97 |
Network Layer | Parameter | Output Size |
---|---|---|
Input | 512 × 1 | |
ConV | Kernel Size = 32; Stride = 4; Channel Size = 24 | 121 × 24 |
Deep_ConV | Kernel Size = 16; Stride = 1; Channel Size = 24; Padding = same | 121 × 24 |
ReLU | ||
BatchNormal | ||
Point_ConV | Kernel Size = 1; Stride = 1; Channel Size = 24; Padding = same | 121 × 24 |
Dropout | Dropout = 0.5 | |
Permute | ||
BiLSTM | Input size = 24; hidden size = 16; batch first = True; bidirectional = True | 2 × 16 |
Permute | ||
AdaptiveAvgPool | ||
Linear | 16 × 2; out features = 5 | 1 × 5 |
Model | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|
CNN-LSTM | 86.99% | 86.78% | 86.87% | 86.78% |
MCCNN | 90.13% | 90.08% | 9011% | 90.08% |
WDCNN | 88.11% | 87.81% | 87.87% | 87.81% |
Bi-LSTM | 76.73% | 76.86% | 76.57% | 76.86% |
CNN | 90.62% | 90.08% | 90.10% | 90.08% |
Proposed Model | 97.64% | 97.46% | 97.46% | 97.46% |
Model | Parameters | FLOPs |
---|---|---|
CNN-LSTM | 6.2 × 104 | 3.2 × 107 |
MCCNN | 5.7 × 104 | 3.2 × 107 |
WDCNN | 8.7 × 104 | 5.1 × 106 |
Bi-LSTM | 1.2 × 106 | 2.1 × 106 |
CNN | 2.5 × 105 | 4.7 × 106 |
Proposed Model | 7.1 × 103 | 4.6 × 106 |
Model | 4DB | 6DB | 8DB |
---|---|---|---|
CNN-LSTM | 61.36% | 68.18% | 58.06% |
MCCNN | 83.88% | 72.52% | 70.87% |
WDCNN | 88.22% | 73.55% | 78.10% |
Bi-LSTM | 69.42% | 66.94% | 70.04% |
CNN | 85.74% | 44.21% | 59.09% |
Proposed model | 88.22% | 87.40% | 91.12% |
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Guo, Y.; Zhou, D.; Chen, H.; Yue, X.; Cheng, Y. Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM. Processes 2024, 12, 1999. https://doi.org/10.3390/pr12091999
Guo Y, Zhou D, Chen H, Yue X, Cheng Y. Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM. Processes. 2024; 12(9):1999. https://doi.org/10.3390/pr12091999
Chicago/Turabian StyleGuo, Yonglin, Di Zhou, Huimin Chen, Xiaoli Yue, and Yuyu Cheng. 2024. "Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM" Processes 12, no. 9: 1999. https://doi.org/10.3390/pr12091999
APA StyleGuo, Y., Zhou, D., Chen, H., Yue, X., & Cheng, Y. (2024). Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM. Processes, 12(9), 1999. https://doi.org/10.3390/pr12091999