Physics and Data Collaborative Root Cause Analysis

Data-driven tools for asset health management face significant challenges, including a lack of understanding of physical principles, difficulty incorporating domain experts’ experiences, and consequently low detection accuracy, leading to trustworthiness issues. Automatically integrating data-driven analysis with human knowledge and experience, as found in literature and maintenance logs, is critically needed. Recent progress in large language models (LLMs) offers opportunities to achieve this goal. However, there is still a lack of work that effectively combines pretrained LLMs with data-driven models for asset health management using industrial time series data as input. This paper presents a framework that integrates our recently proposed data-driven AI with pretrained LLMs to address root cause detection in industrial failure analysis. The framework employs LLMs to analyze outputs from our data-driven root cause analysis models, filtering out less relevant results and prioritizing those that align closely with physical principles and domain expertise. Our innovative approach leverages advanced data-driven analytics and a multi-LLM debate for collaborative decision-making, seamlessly merging data-driven insights with domain knowledge. Specifically, through our proposed self-exclusionary debates among multiple LLMs, biases inherent in single-LLM systems are effectively mitigated, enhancing reliability and stability. Crucially, the framework bridges the gap between data-driven models and physics-informed LLMs, accelerating the interaction between data and knowledge for more informed and realistic decision-making processes.