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.
| Subjects: | Applications (stat.AP); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.22548 [stat.AP] |
| (or arXiv:2604.22548v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2604.22548 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1287/msom.2023.0442
DOI(s) linking to related resources |
From: Xiaowei Yue [view email]
[v1]
Fri, 24 Apr 2026 13:38:08 UTC (4,303 KB)
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