Multi-output Extreme Spatial Model for Complex Aircraft Production Systems

arXiv cs.LG / 4/27/2026

💬 OpinionDeveloper Stack & InfrastructureIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that most machine learning production models focus on average behavior, which is insufficient for analyzing abnormal extreme events that drive major costs in aircraft manufacturing.
It introduces a “multi-output extreme spatial model” that captures complex dependencies by using a bilinear function across two spatial domains for control variables and measurement locations.
The study develops approaches for marginal parameter modeling and modeling extremal dependence to better represent heavy-tailed extreme risks.
To handle high-dimensional multi-output settings, the authors propose a graph-assisted composite likelihood estimation framework with accompanying efficient computational algorithms.
In a composite aircraft production case study, the proposed model delivers stronger predictive performance for extreme events than canonical baseline methods and supports improved risk management and quality/safety operations.

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.