An intelligent monitoring method of underground unmanned electric locomotive loading process based on deep learning method

The intelligent monitoring of electric locomotive loading is crucial in unmanned underground systems. A CNN-based monitoring scheme with migration learning was proposed to address efficiency, abnormality, and data acquisition challenges. Locomotive loading datasets are transformed, augmented, and eq...

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Bibliographic Details
Published in:Cogent engineering 2024-12, Vol.11 (1)
Main Authors: Ren, Zhu-li, Zhang, Jin-long, Yuan, Rui-fu
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Australia
Classification
Coal mining
Computer vision
Data acquisition
Data augmentation
Data processing
Deep learning
Edith Cowan University
Efficiency
electric locomotive
Electric railroads
image recognition
intelligent monitoring
Machine learning
Mines
Mining engineering
Mining industry
Mining, Mineral & Petroleum Engineering
Monitoring
Neural networks
Sanjay Kumar Shukla
Transport & Vehicle Engineering
Tunnelling & Underground Engineering
Underground mine
unmanned driving
Vision systems
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Summary:The intelligent monitoring of electric locomotive loading is crucial in unmanned underground systems. A CNN-based monitoring scheme with migration learning was proposed to address efficiency, abnormality, and data acquisition challenges. Locomotive loading datasets are transformed, augmented, and equalized. Our model improves performance and training by modifying the fully connected layer, using optimized learning rate decay and adaptive algorithms. Training in PyTorch, the optimized VGG19-EL migration network achieves 99.85% recognition for 2-classifications, while the optimized RESNET50-EL migration network achieves 97.3% for 10-classifications. Overall, this study proposes a reliable and efficient model for liberating workers and monitoring locomotive loading.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2024.2307174