Data Management System for a Semiautonomous Shuttle Car for Underground Room and Pillar Coal Mines

In recent years, autonomous solutions in the multidisciplinary field of mining engineering have been an extremely popular applied research topic. This is a result of the increasing demands of society on mineral resources along with the accelerating exploitation of the currently economically viable resources, which lead the mining sector to turn to deeper, more-difficult-to-mine orebodies. An appropriate data management system comprises a crucial aspect of the designing and the engineering of a system that involves autonomous or semiautonomous vehicles. The vast volume of data collected from onboard sensors, as well as from a potential IoT network dispersed around a smart mine, necessitates the development of a reliable data management strategy. Ideally, this strategy will allow for fast and asynchronous access to the data for real-time processing and decision-making purposes as well as for visualization through a corresponding human–machine interface. The proposed system has been developed for autonomous navigation of a coalmine shuttle car and has been implemented on a 1/6th scale shuttle car in a mock mine. It comprises three separate nodes, namely, a data collection node, a data management node, and a data processing and visualization node. This approach was dictated by the large amount of collected data and the need to ensure uninterrupted and fast data management and flow. The implementation of an SQL database server allows for asynchronous, real-time, and reliable data management, including data storage and retrieval. On the other hand, this approach introduces latencies between the data management node and the other two nodes. In general, these latencies include sensor latencies, network latencies, and processing latencies. However, the data processing and visualization module is able to retrieve and process the latest data and make a decision about the next optimal movement of the shuttle car prototype in less than 900 ms. This allows the prototype to navigate efficiently around the pillars without interruptions.

Static Analysis and Topological Optimization of Steering Block for Mining Shuttle

Taking the steering block of mine shuttle car as the research object, a three-dimensional solid model of the steering block of mine shuttle car is established by SolidWorks. Based on ANSYS Workbench, the total deformation and stress of the steering block of Mine shuttle car under tension are obtained. According to the boundary conditions, the steering block is optimized by topology optimization, and the steering block is optimized by the results of topology optimization, and finally verified by static analysis. The results show that the mass of the optimized steering block is reduced by 11.7%. This study provides a reference for comprehensive performance analysis and optimization of Mine shuttle car steering block.

Performance Summary of Continuous Mining Machine Proximity Detection Systems

Since 1984, remote controlled continuous mining machines (CMM) have caused 40 crushing and pinning fatalities in the United States. Due to limited space in the underground environment and visibility needs, CMM operators typically work close to the machine which exposes them to the danger of being struck or pinned by it. Because of these fatalities, the Mine Safety and Health Administration (MSHA) has published a rule requiring proximity detection systems (PDSs) on all CMMs except for full-face machines. To test PDS performance, researchers at the National Institute for Occupational Safety and Health (NIOSH) conducted a series of field tests in underground coal mines throughout the United States on CMMs equipped with PDSs. The field tests collected data under a variety of conditions to evaluate the warning and shutdown zone performance of these systems. A baseline test condition was measured when the machine was operating in non-mining mode. Three additional conditions discussed in this paper include testing of the PDS while the machine was operating in mining mode, examining the possibility of parasitic coupling to the trailing cable, and examining the effects of the presence of a shuttle car. The results of this study indicate that the average warning and stop zones vary minimally between non-mining mode and trailing cable influence measurements, as well as between the mining mode and shuttle car presence tests. A majority of the measurements for warning and stop zones showed repeatability within +/− 5 inches (12.7 cm). Additionally, parasitic coupling to the trailing cable was not experienced during this field testing. However, these results show that the range of stop zone measurements varied by 4.7 ft on average and as much as 11.7 ft in different field sites. This is most likely due to individual preferences by operators during installation when the warning and stop zone distances are set. While a PDS should effectively stop a CMM when an operator gets too close to the machine, the large variations between field test measurements indicate that there is a wide variation of performance established during system installation.

Development of Parameters for Dynamic Modeling of Underground Haulage Vehicles

To address concerns of how mobile proximity detection systems will adapt to underground mobile haulage vehicles, researchers have collected and categorized data on the parameters of 145 mine haulage vehicles in 5 categories including load-haul-dump, shuttle car, roof bolter, haul truck, and mobile coal haulage (face drill, production drill, and others.) Statistical methods were used to determine the appropriate representative vehicle for each category. These representative vehicles’ parameters and characteristics could then be used to develop a dynamic model that predicts their dynamic behavior on an underground haulageway surface. These models can be used in conjunction with worker escapability data and/or interaction with other vehicles to provide insight as to whether or not the proximity detection systems will be adequate for the underground mining workplace.