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

Abstract

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.

Keywords:

• Underground mine
• unmanned driving
• electric locomotive
• intelligent monitoring
• deep learning
• image recognition

REVIEWING EDITOR:

• Sanjay Kumar Shukla
• Edith Cowan University
• Australia

SUBJECTS:

• Mining, Mineral & Petroleum Engineering
• Transport & Vehicle Engineering
• Tunnelling & Underground Engineering

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Thanks to the following organizations who provided basic data and technique support for this research: Changsha Digital Mine Co., Ltd. Besides, the authors also gratefully acknowledge the financial support from the National Natural Science Foundation of China (52204168), Henan Key Laboratory for Green and Efficient Mining & Comprehensive Utilization of Mineral Resources (Henan Polytechnic University) (KCF2201), and the Key R&D and Promotion Projects in Henan Province (222102220027).