Integrating Automatic Speech Recognition and Natural Language Processing with Root Cause Approach to Improve Mining Projects

This research explores the integration of Large Language Models (LLMs) and Automatic Speech Recognition (ASR) technologies into Root Cause Analysis (RCA) to enhance decision-making in complex engineering environments, particularly mining operations. Traditional RCA methods, such as Ishikawa diagrams and the "Five Whys," often face limitations related to scalability, reliance on structured data, and the labor-intensive nature of manual processes. By leveraging advanced AI capabilities, this study presents a novel step by step approach that combines ASR for accurate transcription of unstructured verbal data with LLMs for automated causal analysis and solution generation towards to provide an structured RCA analysis. A specific case study was introduced to validate the novel proposal in real scenario. Moreover, a test with different mining operators was developed to evaluate novelties of research proposal by using the Technology Acceptance Model (TAM) questionnaire, which showed high operator satisfaction and usability. The findings emphasize the potential of AI-driven RCA frameworks in streamlining workflows, reducing cognitive load, and improving decision-making processes.

Keywords: Root cause analysis, Artificial intelligence, Automatic speech recognition, Natural language processing.