A Cross-Platform Web3D Monitoring System of the Three-Machine Equipment in a Fully Mechanized Coalface Based on the Skeleton Model and Sensor Data

Abstract

A fully mechanized coalface is a rugged environment that has poor visibility. The traditional video monitoring system has problems such as a lack of realism, a blurry monitoring effect, and poor reliability. It is an important task to monitor the operations of the three-machine equipment (we will refer to the shearer, hydraulic support, and scraper conveyor as the three-machine equipment) intuitively, accurately, and timely and ensure that it is operating safely. This study proposed a cross-platform Web3D monitoring system for the three-machine equipment. First, the virtual mesh model and skeleton model that was embedded in the mesh model were established according to three-machine ontology and basic motion units. Second, the kinematic model of the three-machine skeleton was established via the inverse kinematic modeling of the hydraulic support and the coordinate calculation of the vertices on the three-machine skeleton. Third, the motion data, which were captured by sensors, were applied to drive the movement of the three-machine skeleton and mesh model. Finally, WebGL, which is the latest Internet graphics standard, was used to render the three-machine models, and the performance of this monitoring system is tested on different equipment in the laboratory. The results of the test show that the three-machine cross-platform monitoring system has splendid performance, and it realizes cross-platform 3D monitoring effectively in the laboratory. In the future, this system will be used as a supervisory tool and be integrated with the traditional monitoring system to monitor the three-machine equipment with the field staff.

Data Mining Technology for Equipment Machinery and Information Network Data Resources

“…Oropeza and Hart based on test bench test data, established a baseline equation using the cubic polynomial fitting method [5]. Lu et al made trend prediction based on the regression equation of engine exhaust temperature and other gas path parameters [6]. Shen et al established a piecewise regression equation between the deviation and the flight cycle to predict the trend of the deviation.…”