Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure

Yikai Chen and Pablo L. Durango-Cohen

Transportation Research Part B: Methodological, 2015, vol. 81, issue P1, 78-102

Abstract: We present a two-part multivariate statistical process control framework to support health-monitoring of transportation infrastructure. The first part consists of estimation of regression and ARIMA–GARCH models to explain, predict, and control for common-cause variation in the data, i.e., changes that can be attributed to usual operating conditions, including traffic loads, environmental effects, and damage when present throughout the data. The second part of the framework consists of using multivariate control charts to simultaneously analyze the standardized innovations of the aforementioned models in order to detect possible special-cause or extraordinary events, such as unique/infrequent traffic, weather, or the onset of damage. The proposed approach revolves around construction of T2 control charts as a framework to jointly monitor the evolution and contemporaneous correlation of a set of measurements. The approach provides significant practical/computational advantages over individual analysis of multiple structural properties, and addresses technical problems stemming from ignoring the relationships among them.

Keywords: Performance infrastructure performance modeling; Structural health monitoring; Multivariate statistical process control; Hotelling T2 control chart; Mason–Young–Tracy (MYT) Decomposition; Principal Component Analysis (PCA) (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.trb.2015.08.012

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