Low-cost Internet of Things (IoT) for monitoring and controlling mining fleet using event classifications and pattern recognition in quarries and small-scale open-pit mines

Abstract

This thesis addresses the challenge of developing a low-cost fleet management system that can be implemented by miners with tight budgets, enabling them to improve their processes, increasing the value of their resources. The Fleet Information System (FIS) proposed in this research offers a practical and affordable solution to fleet management, which is specifically designed to be accessible to a wider range of mining users. The FIS is developed in stages, with each stage introducing an upgraded version. In the initial version, data loggers are installed in the machinery, utilising GNSS and Bluetooth records as inputs to identify mining events. The system then transmits this information, which assists supervisors to visualise crucial insights through a web browser interface. The second version of the FIS is upgraded to incorporate Inertial Measurement Units (IMUs), which unlock additional data and allow for pattern recognition and enhanced classification of mining events. Finally, the last version of the FIS described here includes a bridge in the configuration that enables the data loggers to establish a connection with a remote server, thus providing a commercial solution. This research makes three significant contributions. Firstly, it introduces the concept of a low-cost IoT Fleet Information System, which addresses the financial constraints faced by many miners. Secondly, it proposes a new approach for data storage and forwarding, which aims to reduce the cost of communication infrastructure. Finally, the use of pattern recognition techniques at a detailed level enables the FIS to provide insights into the tasks and events performed by machines, which can be used to optimise fleet management and reduce costs. This research successfully resulted in a spin-off company called Kernow Mining Optimisation - Fleet, which was incorporated in the UK . Overall, this study offers a novel and innovative solution for managing fleets of machines, which has potential applications in various industries. The FIS developed in this research can help miners reduce costs, improve productivity, and redefine ore resources, making it a valuable contribution to the field of fleet management.