Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods

Miguel R. Hernandez-Garcia; Sami F. Masri

doi:10.4028/www.scientific.net/AST.56.501

Paper Titles

Advanced Ultrasonic Structural Monitoring of Waveguides
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Damage Detection in Beam-Like Structures Using Deflections Obtained by Modal Flexibility Matrices
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Development of a Structural Health Monitoring Benchmark Problem for High-Rise Slender Structures
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Continuing Structural Health Monitoring after Repair: Starting from Scratch?
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Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods
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A Numerical Method for the Dynamic Analysis of Buildings Provided with Viscoelastic Devices
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Optimization Algorithms for System Integration
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Linking the Impact Force History with Residual Mechanical Characteristics and NDI - A Smart Way to Perform SHM in Composites
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Embedding Intelligence for RTM Process Monitoring and Control
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HomeAdvances in Science and TechnologyAdvances in Science and Technology Vol. 56Multivariate Statistical Analysis for Detection...

Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods

Abstract:

In this paper, three latent variable methods are implemented in a multivariate statistical analysis scheme for detecting and identifying faults in a multi-sensor network. The proposed methods are applied to a sensor network monitoring a MDOF dynamical system and the results are presented, compared and discussed.