<title>Laser vibration measurement for damage detection on composite materials: advances in signal processing</title>

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.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1237.0486s419 ◽

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.

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Health Monitoring of the Aircraft Structure during a Full Scale Fatigue Test with Use of an Active Piezoelectric Sensor Network

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.220-221.328 ◽

2015 ◽

Vol 220-221 ◽

pp. 328-332

Author(s):

Michal Dziendzikowski ◽

Krzysztof Dragan ◽

Artur Kurnyta ◽

Sylwester Klysz ◽

Andrzej Leski

Keyword(s):

Signal Processing ◽

Damage Detection ◽

Crack Growth ◽

Fatigue Test ◽

Piezoelectric Sensor ◽

Full Scale ◽

Growth Monitoring ◽

Aircraft Structure ◽

System Functioning ◽

Signal Processing Methods

The paper presents an approach to develop a system for fatigue crack growth monitoring and early damage detection in the PZL – 130 ORLIK TC II turbo-prop military trainer aircraft structure. The system functioning is based on elastic waves propagation excited in the structure by piezoelectric PZT transducers. In the paper, a built block approach for the system design, signal processing as well as damage detection is presented. Description of damage detection capabilities are delivered in the paper and some issues concerning the proposed signal processing methods and their application to crack growth estimation models are discussed. Selected preliminary results obtained during the Full Scale Fatigue Test thus far are also presented.

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Signal processing and damage detection in a frame structure excited by chaotic input force

10.1117/12.484012 ◽

2003 ◽

Cited By ~ 1

Author(s):

Liming W. Salvino ◽

Darryll J. Pines ◽

Michael D. Todd ◽

Jonathan Nichols

Keyword(s):

Signal Processing ◽

Damage Detection ◽

Frame Structure ◽

Input Force

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Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

Volume 10B: Structures and Dynamics ◽

10.1115/gt2020-15097 ◽

2020 ◽

Author(s):

Shweta Dabetwar ◽

Stephen Ekwaro-Osire ◽

João Paulo Dias

Keyword(s):

Neural Network ◽

Neural Networks ◽

Composite Materials ◽

Data Fusion ◽

Damage Detection ◽

Convolutional Neural Network ◽

Deep Neural Networks ◽

Mixed Data ◽

Image Encoding ◽

Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

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