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.

翻译：问题定义：机器学习中的数据驱动模型已实现生产系统的高效管理。然而，多数机器学习模型专注于建模均值响应或平均模式，难以适用于研究飞机制造中通常备受关注的异常极端事件。由于重尾分布下的极端事件会导致系统管理产生巨额开销，亟需开发精密极端模型以分析复杂极端风险。极端模型的工程应用通常聚焦于单个极端事件，这对具有相关性的复杂系统而言存在不足。方法/结果：针对多输出响应控制系统，我们提出一种极端空间模型，通过控制变量与测量位置的双空间域双线性函数高效捕捉动态特性。本文研究了边际参数建模与极值依存性，并开发了基于图辅助的复合似然估计高效算法及其计算方案以处理高维输出。在复合飞机制造应用中的结果表明，与传统方法相比，所提模型能实现综合性的极端事件分析且具有更优预测性能。管理启示：本方法展示了如何运用极端空间模型预测极端事件并管理飞机等复杂生产系统的极端风险，有助于提升飞机生产系统及更广领域中的质量管理与运行安全性。