Multi-output Extreme Spatial Model for Complex Aircraft Production Systems

2026-04-24

AI Summary

This study addresses the challenge of modeling rare, extreme anomalies in aircraft manufacturing, which often exhibit heavy-tailed distributions and spatial dependencies among multiple outputs—features poorly captured by conventional machine learning methods. To this end, the authors propose a novel extreme-value spatial model that integrates extreme value theory with multi-output spatial modeling. The approach employs a bilinear function to characterize dynamic interactions between control variables and measurement locations across two spatial domains and introduces a graph-assisted composite likelihood estimation method to handle high-dimensional outputs. The resulting framework jointly models marginal extreme behaviors and extremal dependence structures among outputs, supported by an efficient computational algorithm. Experiments on a composite-material aircraft production system demonstrate that the proposed method significantly outperforms existing techniques in predicting extreme events, thereby enhancing quality control and operational safety.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Graphical Models
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Problem definition: Data-driven models in machine learning have enabled efficient management of production systems. However, a majority of machine learning models are devoted to modeling the mean response or average pattern, which is inappropriate for studying abnormal extreme events that are often of primary interest in aircraft manufacturing. Since extreme events from heavy-tailed distributions give rise to prohibitive expenditures in system management, sophisticated extreme models are urgently needed to analyze complex extreme risks. Engineering applications of extreme models usually focus on individual extreme events, which is insufficient for complex systems with correlations. Methodology/results: We introduce an extreme spatial model for multi-output response control systems that efficiently captures the dynamics using a bilinear function on two spatial domains for control variables and measurement locations. Marginal parameter modeling and extremal dependence have been investigated. In addition, an efficient graph-assisted composite likelihood estimation and corresponding computational algorithms are developed to cope with high-dimensional outputs. The application to composite aircraft production shows that the proposed model enables comprehensive analyses with superior predictive performance on extreme events compared to canonical methods. Managerial implications: Our method shows how to use an extreme spatial model for predicting extreme events and managing extreme risks in complex production systems such as aircraft. This can help achieve better quality management and operation safety in aircraft production systems and beyond.

Problem

• extreme events
• multi-output
• spatial model
• heavy-tailed distributions
• extremal dependence

Innovation

• extreme spatial model
• multi-output response
• extremal dependence
• graph-assisted composite likelihood
• bilinear function