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Control charts for monitoring ship operating conditions and CO 2 emissions based on scalar‐on‐function regression
Author(s) -
Capezza Christian,
Lepore Antonio,
Menafoglio Alessandra,
Palumbo Biagio,
Vantini Simone
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2507
Subject(s) - scalar (mathematics) , multivariate statistics , context (archaeology) , computer science , operations research , functional data analysis , field (mathematics) , environmental science , anomaly detection , data mining , engineering , mathematics , geology , machine learning , geometry , paleontology , pure mathematics
To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, a novel procedure is proposed, which extends classical multivariate techniques to the monitoring of multivariate functional data and a scalar quality characteristic related to them. The proposed procedure is shown to be also applicable in real time and is illustrated by means of a real‐case study in the maritime field on the continuous monitoring of operating conditions (ie, the multivariate functional data) and total CO 2 emissions (ie, the scalar quality characteristic) at each voyage of a cruise ship. The real‐time monitoring is particularly helpful for promptly supporting managerial decision making by indicating if and when an anomaly occurs during the navigation.