Design of Experiment Issues in Material Component Damage Detection Including Sensor Mass and Footprint Studies

2007 ◽  
Author(s):  
Jonathan R. White ◽  
Douglas E. Adams ◽  
Shankar Sundararaman ◽  
Carlos Escobar
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Element Model ◽  
Field Analysis ◽  
Measurement Variability ◽  
Structural Health ◽  
Material Component ◽  
Sensor Mass ◽  
Honeycomb Panel

In structural health monitoring, the reliability of measured data for use in damage detection is greatly influenced by the sensor characteristics. Specific issues related to design of experiments in structural health monitoring are analyzed including sensor attachment, mass, frequency range, and footprint. A circular plate instrumented with various sensors and attachment methods (adhesive, stud) is used to study the effects of sensor bond on dynamic measurements. Attachment types are shown to have different amounts of measurement variability using an ANalysis Of VAriance technique. Differences in the amount of measurement variability lead to different damage detection thresholds. A validated sandwich metallic honeycomb panel finite element model is also used to conduct a numerical sensitivity analysis. Sensor mass is shown to reduce the sensitivity of damage detection for realistic sensor masses. The sensor footprint is shown to become an issue, by an acceleration field analysis, when the wavelength of the frequency of excitation is on the order of the sensor diameter.

scholarly journals Magnetostrictive Sensors for Composite Damage Detection and Wireless Structural Health Monitoring

2019 ◽  
Vol 55 (7) ◽  
pp. 1-6
Author(s):  
Zhaoyuan Leong ◽  
William Holmes ◽  
James Clarke ◽  
Akshay Padki ◽  
Simon Hayes ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Health ◽  
Composite Damage

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.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

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.

High-performance fiber optic systems for damage detection and structural health monitoring

2004 ◽  
Author(s):  
Mark E. Seaver ◽  
Stephen T. Trickey ◽  
Jonathan M. Nichols ◽  
Linda Moniz ◽  
Lou Pecora ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
High Performance ◽  
Fiber Optic ◽  
Structural Health ◽  
High Performance Fiber ◽  
Optic Systems

Method for removing temperature effect in impedance-based structural health monitoring systems using polynomial regression

2020 ◽  
pp. 147592172091712 ◽  
Author(s):  
Bárbara M Gianesini ◽  
Nicolás E Cortez ◽  
Rothschild A Antunes ◽  
Jozue Vieira Filho
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Temperature Effect ◽  
Health Monitoring ◽  
Monitoring Systems ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Detection Systems ◽  
Impedance Technique ◽  
Health Monitoring Systems

Structural health monitoring systems are employed to evaluate the state of structures to detect damage, bringing economical and safety benefits. The electromechanical impedance technique is a promising damage detection tool since it evaluates structural integrity by only measuring the electrical impedance of piezoelectric transducers bonded to structures. However, in real-world applications, impedance-based damage detection systems exhibit strong temperature dependence; therefore, variations associated with temperature changes may be confused as damage. In this article, the temperature effect on the electrical impedance of piezoelectric ceramics attached to structures is analyzed. Besides, a new methodology to compensate for the temperature effect in the electromechanical impedance technique is proposed. The method is very general since it can be applied to nonlinear (polynomial) temperature and/or frequency dependences observed on the horizontal and vertical shifts of the impedance signatures. A computer algorithm that performs the compensation was developed, which can be easily incorporated into real-time damage detection systems. This compensation technique is applied successfully to two aluminum beams and one steel pipe, minimizing the effect of temperature variations on damage detection structural health monitoring systems in the temperature range from −40°C to 80°C and the frequency range from 10 to 90 kHz.

Structural Health Monitoring using deep learning with optimal finite element model generated data

2020 ◽  
Vol 145 ◽  
pp. 106972 ◽  
Author(s):  
Panagiotis Seventekidis ◽  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Olga Markogiannaki
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Element Model ◽  
Structural Health
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