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Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks
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  • Journal title : Smart Structures and Systems
  • Volume 17, Issue 6,  2016, pp.1031-1053
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2016.17.6.1031
 Title & Authors
Canonical correlation analysis based fault diagnosis method for structural monitoring sensor networks
Huang, Hai-Bin; Yi, Ting-Hua; Li, Hong-Nan;
 Abstract
The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.
 Keywords
structural health monitoring;sensor fault diagnosis;canonical correlation analysis;dynamic or auto-regressive characteristic;contribution analysis;
 Language
English
 Cited by
1.
Development of sensor validation methodologies for structural health monitoring: A comprehensive review, Measurement, 2017, 109, 200  crossref(new windwow)
2.
Bayesian Combination of Weighted Principal-Component Analysis for Diagnosing Sensor Faults in Structural Monitoring Systems, Journal of Engineering Mechanics, 2017, 143, 9, 04017088  crossref(new windwow)
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