Dynamic cooperation and mutual feedback network for shield machine

Abstract

A shield machine (SM) is a complex mechanical device used for tunneling. Traditional methods of monitoring SM working conditions relied on artificial experience, which was prone to hidden mechanical failures, human operator errors, and sensor anomalies. Faced with this situation, many scholars have studied intelligent control and decision-making methods for SM. However, most of these methods do not consider the relationship between SM and the operating environment, which makes it difficult for the model to apply to the actual engineering. In order to overcome these limitations, this paper proposes a dynamic cooperation and mutual feedback network based on the Internet of Things (IoT). The network integrates SM, geological information, and control terminals. Then, based on this network, for the two important tasks of the SM field, rate prediction and anomaly detection has established SM Geology Convolutional with Attention (SM-GCA) and SM Geology Variational Autoencoder with Long Short-Term Memory (SM-GVL) models respectively. Our experimental results demonstrate that our proposed two models outperform baseline models in terms of performance.