Prediction of mechanical characteristics of shearer intelligent cables under bending conditions
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| Опубліковано в:: | PLoS One vol. 20, no. 2 (Feb 2025), p. e0318767 |
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| Автор: | |
| Інші автори: | , , , , |
| Опубліковано: |
Public Library of Science
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| Онлайн доступ: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0318767 |2 doi | |
| 035 | |a 3163422629 | ||
| 045 | 2 | |b d20250201 |b d20250228 | |
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| 100 | 1 | |a Zhao, Lijuan | |
| 245 | 1 | |a Prediction of mechanical characteristics of shearer intelligent cables under bending conditions | |
| 260 | |b Public Library of Science |c Feb 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN’s causal and dilated convolution operations to capture long-term sequential features, BiLSTM’s bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model’s performance is validated through comparisons with multiple other models, and the contributions of input features to the model’s predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions. | |
| 653 | |a Mechanical properties | ||
| 653 | |a Bending fatigue | ||
| 653 | |a Mines | ||
| 653 | |a Coal mining | ||
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Fiber optics | ||
| 653 | |a Predictive control | ||
| 653 | |a Service life | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Optical memory (data storage) | ||
| 653 | |a Time series | ||
| 653 | |a Prediction models | ||
| 653 | |a Efficiency | ||
| 653 | |a Optical properties | ||
| 653 | |a Machine learning | ||
| 653 | |a Cables | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Network reliability | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Neural networks | ||
| 653 | |a Bending machines | ||
| 653 | |a Working conditions | ||
| 653 | |a Optical fibers | ||
| 653 | |a Conductors | ||
| 653 | |a Information processing | ||
| 653 | |a Coal mines | ||
| 653 | |a Safety management | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wang, Dongyang | |
| 700 | 1 | |a Lin, Guocong | |
| 700 | 1 | |a Tian, Shuo | |
| 700 | 1 | |a Zhang, Hongqiang | |
| 700 | 1 | |a Wang, Yadong | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 2 (Feb 2025), p. e0318767 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3163422629/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3163422629/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3163422629/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |