Multioutput Extreme Spatial Model for Complex Aircraft Production Systems

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, and this is inappropriate for studying abnormal extreme events that are often of primary interest in aircraft manufacturing. Because 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, and this is insufficient for complex systems with correlations. Methodology/results: We introduce an extreme spatial model for multioutput 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 with 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. Funding: This work was supported by the National Natural Science Foundation of China [Grant 92467302], the Beijing Natural Science Foundation [Grant L241039], the Opening Project Fund of Materials Service Safety Assessment Facilities, and the National Academy of Sciences (Grainger Frontiers of Engineering Award).