Design and Implementation of a Data Validation System for a Virtual Underground Drilling Machine

Abstract

This thesis presents the design and implementation of a virtualized data validation system for longhole underground drilling machines. Conducted in collaboration with Sandvik, the work addresses the growing need for automated and reliable testing of drilling control systems in situations where physical hardware is not used for testing. The aim is to improve testing capabilities through a Docker-based, modular virtual testing environment integrated with automated data collection, processing, and visualization.

The system utilizes Robot Framework for test automation, Docker Compose for environment orchestration, and custom Python libraries for data parsing and reporting. The virtual environment emulates ideal drilling conditions, enabling repeatable, scalable, and hardware-independent testing. Key components include a binary file parser that converts drilling data into JSON format and a reporting tool used to analyze data integrity and system behavior.

In the validation phase, the similarity between the virtual and real simulation environments was evaluated, particularly through latency measurements. Additionally, the robustness of the virtual environment was examined using data consistency checks and statistical analyses. The results demonstrate that the system is capable of detecting software errors early in the development cycle. This work highlights both the benefits and challenges of simulation-based testing and provides a foundation for future development, such as continuous integration-driven testing processes.