Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

Abstract

Early detection of cracks enables timely mitigation and maintenance actions, ensuring the safety of personnel and equipment within the open-pit coal mine. Monitoring open-pit coal mines and cracks is essential for the safety of workers and for saving national assets. Digital twins (DTs) can be crucial in open-pit coal mine crack detection. DTs enable continuous real-time monitoring of the open-pit mine, including its structures and surrounding environment. Various sensors and internet-of-things devices can be deployed to collect data on factors such as ground movement and strain. Integrating this data into the DT makes it possible to identify and analyze anomalous behavior or changes that may indicate crack formation or propagation. Deep learning-based networks are a crucial factor in detecting open-pit coal mine cracks. In this work, we propose a deep learning-based densely connected lightweight network incorporated into the DT-based framework for detecting cracks and taking predictive maintenance-based decisions by combining historical data, real-time sensor data, and predictive models. The proposed DT-based framework provides insights into the potential crack formation, allowing for proactive maintenance and mitigation measures. We compare the performance of our proposed network on different evaluation measures such as precision, recall, overall accuracy, mean average precision, F1-score, and kappa coefficient, where our proposed lightweight multiscale feature fusion-based network outperformed all other state-of-the-art deep neural networks. We also achieved the best performance on mean average precision by surpassing all other models. Additionally, we also compared the performance of our proposed network with U-Net and recurrent neural network on model training and prediction time benchmarks by outperforming those cutting-edge models.