Research Status and Prospects of Auto-height Adjustment Strategy for Shearer

Abstract

The development of autonomous shearer height adjustment technology, a crucial component of generalized mining automation, is covered in this study. This study examines the main technical development research in the two directions of coal-rock interface detection and memory cutting in order to investigate the development of shearer auto-height adjustment technology. The development of five methods, such as image recognition method, is introduced in detail in coal rock identification. It lists the shortcomings of each approach and provides an overview of the major variables influencing the advancement of shearer auto-height adjustment technology. Based on the current state of height adjustment technology development and the demand for coal mine intelligence, the following development outlook for auto-height adjustment of shearers is suggested: integrating a variety of cutting-edge technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data (Big Data), along with the safety mechanism, to create a more complete and effective auto-height adjustment system for shearers. The article concludes by highlighting ongoing research in this area, which uses data expansion to address the issue of poor data quality while also allowing for the combination of machine learning algorithms, data expansion by the appropriate network model to train high-quality and high-precision models, and the development of memory cutting technology to create a comprehensive, continuous, and accurate independent height adjustment control system of the shearer.