scholarly journals A comparative study of signal processing methods for structural health monitoring

2016 ◽  
Vol 18 (4) ◽  
pp. 2186-2204 ◽  
Author(s):  
Hojjat Adeli ◽  
Hossein Qarib
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Comparative Study ◽  
Health Monitoring ◽  
Processing Methods ◽  
Structural Health ◽  
Signal Processing Methods

Time–frequency and time–scale analyses for structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 449-477 ◽  
Author(s):  
Wiesław J Staszewski ◽  
Amy N Robertson
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Time Scale ◽  
Time Frequency ◽  
Structural Health ◽  
Variant Analysis

Signal processing is one of the most important elements of structural health monitoring. This paper documents applications of time-variant analysis for damage detection. Two main approaches, the time–frequency and the time–scale analyses are discussed. The discussion is illustrated by application examples relevant to damage detection.

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

scholarly journals Comparative Study of Fiber Bragg Gratings and Fiber Polarimetric Sensors for Structural Health Monitoring of Carbon Composites

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Massimo Olivero ◽  
Guido Perrone ◽  
Alberto Vallan ◽  
Daniele Tosi
Keyword(s):  
Structural Health Monitoring ◽  
Comparative Study ◽  
Health Monitoring ◽  
Smart Materials ◽  
Vibration Monitoring ◽  
Carbon Fiber Composites ◽  
Test Plate ◽  
Structural Health ◽  
Dynamic Excitation ◽  
Cheap Alternative

A comparative study is presented between Bragg grating (FBG) and polarimetric sensors (PS), two of the most promising fiber optic sensing techniques for the structural health monitoring of smart materials based on carbon fiber composites. The paper describes the realization of a test plate equipped with both types of sensors and reports the characterization under static and dynamic conditions, highlighting pros and cons of both technologies. The FBG setup achieves 1.15 ± 0.0016 pm/kg static load response and reproduces dynamic excitation with 0.1% frequency uncertainty; the PS system exhibits a sensitivity of 1.74 ± 0.001 mV/kg and reproduces dynamic excitation with 0.5% frequency uncertainty. It is shown that the PS technology is a good and cheap alternative to FBG for vibration-monitoring of small structures at high frequency.

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