Robust Autonomous Damage Detection and Assessment in Polymeric Composite Structures

2008 ◽  
Author(s):  
Ajay Kesavan ◽  
Sabu John ◽  
Henry Li ◽  
Israel Herszberg
Keyword(s):  
Damage Detection ◽  
Composite Structures ◽  
Research Study ◽  
Polymeric Composite ◽  
Variable Loading ◽  
Damage Scenarios ◽  
Damage Location ◽  
Joint Structure ◽  
Analytical Work ◽  
Load Angle

This paper introduces the some of the experimental and analytical work behind the autonomous damage detection technique. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2-D polymeric composite T-joint, used in maritime structures. Two methods of damage detection are discussed — A statistics-based outlier technique and one using Artificial Neural Networks (ANNs). The SHM using ANNs system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all the delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this paper.

Damage Detection Based on Adaptive Estimation of Amplitude and Phase

2008 ◽  
Author(s):  
Yousof Azizi ◽  
David M. McNamara ◽  
Alireza K. Ziarani ◽  
Ratneshwar Jha
Keyword(s):  
Experimental Data ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Adaptive Estimation ◽  
Tracking Algorithm ◽  
Monitoring Method ◽  
Damage Scenarios ◽  
Damage Location ◽  
Damage Indices

This paper presents a novel structural health monitoring method that shows promise in identifying damage location within a structure while providing some means of indicating the severity of the damage. The proposed method is based on an adaptive sinusoid-tracking algorithm (STA), which is capable of tracking sinusoid parameters, namely amplitude, phase, and frequency. The proposed method makes use of the estimated amplitude and phase parameters obtained by processing strain recordings at individual sensors located along an excited structure to identify damage locations and severity. Damage indices have been developed that compare the tracked amplitude/phase from a baseline (or healthy) case to the tracked amplitude/phase from the structure under test at specific locations. The proposed methodology is applied to both simulated and experimental plates in a number of damage scenarios. The results of both simulated and experimental data indicate that the proposed method is promising in the detection of the damage, its location and its severity.

scholarly journals Local and Global Approaches for Damage Detection in Composite Structures by Fiber Optic Sensors

2018 ◽  
Author(s):  
Alfredo Guemes ◽  
Antonio Fernandez- Lopez ◽  
Patricia F. Diaz-Maroto ◽  
Angel Lozano ◽  
Julián Sierra-Pérez
Keyword(s):  
Damage Detection ◽  
Composite Structures ◽  
Fiber Optic ◽  
A Priori ◽  
Fiber Optic Sensors ◽  
Network Density ◽  
Strain Measurements ◽  
Damage Location ◽  
Local Technique ◽  
External Loads

Fiber optic sensors cannot measure damage; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two independent concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure, it is based on identifying the changes caused by the damage on the strains field in the whole structure for similar external loads. Damage location does not need to be known a priori, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given.

scholarly journals A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

2013 ◽  
Vol 20 (4) ◽  
pp. 633-648 ◽  
Author(s):  
Zahra Tabrizian ◽  
Ehsan Afshari ◽  
Gholamreza Ghodrati Amiri ◽  
Morteza Hossein Ali Beigy ◽  
Seyed Mohammad Pourhoseini Nejad
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Element Model ◽  
Optimization Method ◽  
Big Bang ◽  
Continuous Variable ◽  
Particle Swarm Optimizer ◽  
Damage Scenarios ◽  
Damage Location ◽  
Big Crunch

The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB-BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

DAMAGE DETECTION IN LAMINATED COMPOSITES BY NEURAL NETWORKS AND HIGH ORDER FINITE ELEMENTS

2021 ◽  
Author(s):  
ALFONSO PAGANI ◽  
MARCO ENEA ◽  
ERASMO CARRERA
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Composite Laminates ◽  
Composite Structures ◽  
Laminated Composite ◽  
Machine Learning Techniques ◽  
Damage Scenarios ◽  
Complete Mapping ◽  
Vibrational Characteristics ◽  
Local Defects

In the aerospace industry, machine learning techniques are becoming more and more important for Structural Health Monitoring (SHM). In fact, they could be useful in giving a precise and complete mapping of damage distribution in a structure, including low-intensities or local defects, which cannot be detected via traditional tests. In this work, feedforward artificial neural networks (ANN) are employed for vibration-based damage detection in composite laminates. In the framework of Carrera Unified formulation (CUF), one-dimensional refined models in conjunction with layer-wise (LW) theory are adopted. CUF-based Monte Carlo simulations have been used for the creation of a dataset of damage scenarios for the training of the ANN. Therefore, the latter is fed with the vibrational characteristics of these structures. The trained ANN, given these dynamic parameters, is able to predict location and intensity of all damages in the laminated composite structures.

Experimental Research on Damages Detection of Pipeline Structure by Using PZT-Based Waves

2011 ◽  
Author(s):  
Shi Yan ◽  
Binbin He ◽  
Naizhi Zhao
Keyword(s):  
Damage Detection ◽  
Lead Zirconate Titanate ◽  
Smart Materials ◽  
Arrival Time ◽  
Mode Conversion ◽  
Guided Wave ◽  
The Other ◽  
Damage Location ◽  
The Waves ◽  
Peak Value

Pipeline structure may generate damages during its service life due to the influence of environment or accidental loading. The damages need to be detected and repaired if they are severe enough to influence the transportation work. Non-destructive detection using smart materials combined with suitable diagonal algorithms are widely used in the field of structural health monitoring (SHM). Piezoelectric ceramics (such as Lead Zirconate Titanate, PZT) is one of the smart materials to be applied in the SHM due to the piezoelectric effect. So far, the PZT-based wave method is widely used for damage detection of structures, in particular, pipeline structures. A series of piezoelectric patches are bonded on the surface of the pipeline structure to monitor the damages such as local crack or effective area reduction due to corrosion by using diagonal waves. The damage of the pipeline structure can be detected by analysis of the received diagonal waves which peak value, phase, and arriving time can be deferent from the health ones. The response of the diagonal wave is not only correlated to the damage location through estimation of the arrival time of the wave peak, but also associated with the peak value of the wave for the reduction of wave energy as the guided wave passing through the damages. Therefore, the presence of damages in the pipeline structure can be detected by investigating the parameter change of the guided waves. The change of the wave parameters represents the attenuation, deflection and mode conversion of the waves due to the damages. In addition, the guided wave has the ability of quick detecting the damage of the pipeline structure and the simplicity of generating and receiving detection waves by using PZT patches. To verify the proposed method, an experiment is designed and tested by using a steel pipe bonded the PZT patches on the surface of it. The PZT patches consist of an array to estimate the location and level of the damage which is simulated by an artificial notch on the surface of the structure. The several locations and deep heights of the notches are considered during the test. A pair of the PZT patches are used at the same time as one is used as an actuator and the other as a sensor, respectively. A tone burst of 5 cycles of wave shape is used during the experiment. A wave generator is applied to create the proposed waves, and the waves are amplified by an amplifier to actuate the PZT patch to emit the diagonal waves with appropriately enough energy. Meanwhile, the other PZT patch is used as a sensor to receive the diagonal signals which contain the information of the damages for processing. For data processing, an index of root mean square deviation (RMSD) of the received data is used to estimate the damage level by compare of the data between the damaged and the health peak valves of the received signals. The time reversal method which aimed at increasing the efficiency of the detection is also used to detect the damage location by estimating the arrival time of the reflected wave passing with a certain velocity. The proposed method experimentally validates that it is effective for application in damage detection of pipeline structure.

Study on Damage Detection of Bridge Based on Wavelet Multi-Scale Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 1010-1014 ◽  
Author(s):  
Ke Ding ◽  
Ting Peng Chen
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Detection Method ◽  
Mode Shapes ◽  
Scale Analysis ◽  
Multi Scale ◽  
Damage Location ◽  
Transform Coefficients ◽  
Beam Bridge ◽  
Multi Scale Analysis

The damage detection method based on wavelet multi-scale analysis is presented in the paper. The damage location can be identified by analyzing the multi-scale wavelet transform coefficients of curvatures of mode shapes. The extreme value of wavelet transform coefficients indicates the damage location. But it is difficult to detect the location of defect if the defect is near to the equilibrium position of vibration. In order to solve this problem, we put forward a method which is to add the wavelet transform coefficients of multi modals together. The method can effectively overcome the above problem. Three damage situations of simply supported beam bridge are discussed in the paper. The results show that the peaks of wavelet transform coefficients indicate the damage location of structural. It is possible to pinpoint the damage location based on wavelet multi-scale analysis on curvatures of mode shapes.

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