Deriving inferences through natural language from structured datasets for asset lifecycle management

Abstract

Industry 4.0 is expected to revolutionize the manufacturing world in terms of improved decision-making leading to increased value extraction from assets. Yet, since its inception, its adoption seems to have not scaled with time as expected. One of the bottlenecks can be due to the knowledge disparity between the Industry 4.0 technology developer and the domain expert/end user, which can lead to higher resources, cost, and time requirements for scalable adoption. Modern day Large Language Models (LLMs) have the ability to process large datasets and can serve as an intermediatory tool for domain end-users. Thus, in this paper, an Industrial-GPT tool is developed to translate natural language queries into meaningful inferences about asset-related data. The tool is evaluated using an exemplary dataset, representing a manufacturing industry with four production lines, multiple assets, readings and inferences from multiple sensors. The experimental results show that reasonable insight may be extracted from the dataset, using prompt engineering, while fine-tuning the model to adapt to business logic can help achieve better and faster inferences. Future research directions are proposed that can be addressed while developing Industrial-GPT-based tools and reducing the development cycle for Industry 4.0 technologies. Copyright © 2024 The Authors.