A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phase shift in signals caused by tempera...

Full description

Saved in:
Bibliographic Details
Published in:Structural health monitoring 2016-09, Vol.15 (5), p.525-540
Main Authors: Fendzi, C, Rébillat, M, Mechbal, N, Guskov, M, Coffignal, G
Format: Article
Language:English
Subjects:
Acoustics
Artificial Intelligence
Computer Science
Engineering Sciences
Estimating
Lamb waves
Least squares method
Mathematical models
Mechanics
Parameter estimation
Parameters
Signal and Image Processing
Structural health monitoring
Temperature compensation
Vibrations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phase shift in signals caused by temperature changes. An ordinary least square (OLS) algorithm is used to estimate these unknown parameters. After estimating these parameters at each temperature in the operating range, linear functional relationships between the temperature and the estimated parameters are derived using the least squares method. A temperature compensation model is developed based on this linear relationship that allows one to reconstruct sensor signals at any arbitrary temperature. The proposed approach is validated numerically and experimentally for an anisotropic composite plate at different temperatures ranging from 16 ° C to 85 ° C . A close match is found between the measured signals and the reconstructed ones. This approach is interesting as it needs only a limited set of piezo-sensor signals at different temperatures for model training and temperature compensation at any arbitrary temperature. Damage localization results after temperature compensation demonstrate its robustness and effectiveness.
ISSN:1475-9217
1741-3168
DOI:10.1177/1475921716650997