A Data-Driven Method for Predicting The Operational Status of The Aircraft Assembly System

Abstract

Aircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The proposed methodology facilitates timely anomaly detection and early warning, allowing proactive risk mitigation and ensuring sustained production system stability. This research contributes to advancing intelligent monitoring and control strategies for aircraft assembly operations within data-driven manufacturing environments.