Evaluating Large Language Models for Turboshaft Engine Torque Prediction

Recent advancements in deep learning have introduced new opportunities for quality management in manufacturing, particularly through transformer-based architectures capable of learning from limited datasets and handling complex, multimodal inputs. Among these, Large Language Models (LLMs) have emerged as a significant innovation, demonstrating strong capabilities in forecasting and representing the cutting edge of artificial intelligence (AI). Through transfer learning, LLMs effectively process and generate extended text sequences, and recent developments show their potential for multimodal integration, including text, images, audio, and video data. Quality management is a critical area for industrial innovation, rapidly evolving as manufacturers seek to close the quality-manufacturing loop and achieve zero-defect production goals. While computer vision techniques based on deep learning have been widely implemented for visual inspection tasks, integrating multiple heterogeneous data sources offers the possibility for even greater improvements. Despite the success of LLMs in language tasks, their application to time series data remains relatively unexplored. Alternative statistical approaches and deep learning models have proven effective for time series forecasting. Nevertheless, LLMs could provide additional advantages in industrial contexts, offering opportunities to enhance in-line quality control, defect prevention, and predictive discarding strategies across various sectors. This paper investigates the potential of applying LLMs to time series analysis by comparing the performance of an LLM (GPT-2), originally trained on textual data, with a model specifically designed for time series data (TimeGPT), and a more conventional transformer-based architecture. Our study includes a dedicated time series GPT model and a general-purpose LLM in a comparative evaluation. Through this analysis, we aim to better understand how language models can be effectively adapted to time series forecasting tasks and explore their transfer learning potential for enhancing quality management in manufacturing.