Damage localization using warped frequency transform in active structural health monitoring

2015 ◽  
Vol 47 (4) ◽  
pp. 897-909 ◽  
Author(s):  
Chao Zhang ◽  
Jinhao Qiu ◽  
Hongli Ji ◽  
Shengbo Shan ◽  
Jinling Zhao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Localization ◽  
Structural Health

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

Wind Energy ◽  
2018 ◽  
Vol 21 (8) ◽  
pp. 676-680 ◽  
Author(s):  
Philip Arnold ◽  
Jochen Moll ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Damage Localization ◽  
Wind Turbine Blades ◽  
Structural Health

scholarly journals Damage localization in geometrically complex aeronautic structures using canonical polyadic decomposition of Lamb wave difference signal tensors

2019 ◽  
Vol 19 (1) ◽  
pp. 305-321
Author(s):  
Marc Rébillat ◽  
Nazih Mechbal
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Health State ◽  
Damage Localization ◽  
Localization Algorithm ◽  
Structural Health ◽  
Piezoelectric Elements ◽  
Geometrically Complex ◽  
Canonical Polyadic Decomposition

Monitoring in real time and autonomously the health state of aeronautic structures is referred to as structural health monitoring and is a process decomposed in four steps: damage detection, localization, classification, and quantification. In this work, the structures under study are aeronautic geometrically complex structures equipped with a bonded piezoelectric network. When interrogating such a structure, the resulting data lie along three dimensions (namely, the “actuator,”“sensor,” and “time” dimensions) and can thus be interpreted as three-way tensors. The fact that Lamb wave structural health monitoring–based data are naturally three-way tensors is here investigated for damage localization purpose. In this article, it is demonstrated that under classical assumptions regarding wave propagation, the canonical polyadic decomposition of rank 2 of the tensors build from the phase and amplitude of the difference signals between a healthy and damaged states provides direct access to the distances between the piezoelectric elements and damage. This property is used here to propose an original tensor-based damage localization algorithm. This algorithm is successfully validated on experimental data coming from a scale one part of an airplane nacelle (1.5 m in height for a semi circumference of 4 m) equipped with 30 piezoelectric elements and many stiffeners. Obtained results demonstrate that the tensor-based localization algorithm can locate a damage within this structure with an average precision of 10 cm and with a precision lower than 1 cm at best. In comparison with standard damage localization algorithms (delay-and-sum, reconstruction algorithm for probabilistic inspection of defects, and ellipse- or hyperbola-based algorithms), the proposed algorithm appears as more precise and robust on the investigated cases. Furthermore, it is important to notice that this algorithm only takes the raw signals as inputs and that no specific pre-processing steps or finely tuned external parameters are needed. This algorithm is thus very appealing as reliable and easy to settle damage localization timeliness with low false alarm rates are one of the key successes to shorten the gap between research and industrial deployment of structural health monitoring processes.

Removing the Ambiguity in Lamb Wave-Based Damage Localization

2009 ◽  
Vol 413-414 ◽  
pp. 79-86 ◽  
Author(s):  
Pawel Malinowski ◽  
Tomasz Wandowski ◽  
Wiesław M. Ostachowicz
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Phased Array ◽  
Lamb Waves ◽  
Theoretical Calculations ◽  
Signal To Noise Ratio ◽  
Damage Localization ◽  
Monitoring Method ◽  
Structural Health ◽  
Starting Point

The aim of this work is the investigation and improvement of a Structural Health Monitoring method based on Lamb waves propagation. This research concentrates on ambiguity in damage localization using attached piezoelectric transducers as sources and sensors of the elastic waves. A linear phased array is chosen as a starting point of the investigation. It has a great advantage in damage localization, namely it enables to amplify the wave reflected from damage, increasing the signal to noise ratio, and precisely indicates not only the distance to damage from the array but also the direction on which the damage lies. However it has also a great disadvantage which needs to be handled – the localization results are symmetric in relation to the line on which the transducers of linear phased array are placed. This obviously does not facilitate Structural Health Monitoring process and precise indication of damage placement. Therefore this investigation aims to improve this localization method by removing the ambiguity in results. In this work the placement of transducers forming a linear phased array is modified to achieve this goal. Several array modification are investigated and compared in order to determine the best solution. Presented research is based on theoretical calculations as well as laboratory experiments on prepared specimens. The measurements are conducted with a compact 13–channel SHM system controlled by a MATLAB® script.

Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks

2019 ◽  
Vol 19 (6) ◽  
pp. 1685-1710 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar ◽  
Stefano Mariani
Keyword(s):  
Feature Extraction ◽  
Structural Health Monitoring ◽  
Probability Measure ◽  
Health Monitoring ◽  
Damage Localization ◽  
Order Selection ◽  
Computational Burden ◽  
Structural Health ◽  
Leibler Divergence ◽  
Empirical Probability

Data-driven damage localization is an important step of vibration-based structural health monitoring. Statistical pattern recognition based on the prominent steps of feature extraction and statistical decision-making provides an effective and efficient framework for structural health monitoring. However, these steps may become time-consuming or complex when there are large volumes of vibration measurements acquired by dense sensor networks. To deal with this issue, this study proposes fast unsupervised learning methods for feature extraction through autoregressive modeling and damage localization through a new distance measure called Kullback–Leibler divergence with empirical probability measure. The feature extraction approach consists of an iterative algorithm for order selection and parameter estimation aiming to extract residuals in the training phase and another iterative process aiming to extract residuals only in the monitoring phase. The key feature of the proposed approach is the use of correlated residual samples of the autoregressive model as a new time series at each iteration, rather than handling the measured vibration response of the structure. This is shown to highly reduce the computational burden of order selection and feature extraction; moreover, it effectively provides low-order autoregressive models with uncorrelated residuals. The Kullback–Leibler divergence with empirical probability measure method exploits a segmentation technique to subdivide random data into independent sets and provides a distance metric based on the theory of empirical probability measure with no need to explicitly compute the actual probability distributions at the training and monitoring stages. Numerical and experimental benchmarks are then used to assess accuracy and performance of the proposed methods and compare them with some state-of-the-art approaches. Results show that the proposed approaches are successful in feature extraction and damage localization, with a reduced computational burden.

Reference-free damage localization in time-space domain for structural health monitoring of X-COR sandwich composites

2018 ◽  
Vol 30 (3) ◽  
pp. 371-385 ◽  
Author(s):  
Guoyi Li ◽  
Aditi Chattopadhyay
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Localization ◽  
Sandwich Composites ◽  
Space Domain ◽  
Space Analysis ◽  
Structural Health ◽  
Time Space ◽  
Wide Range ◽  
Excitation Frequencies

This article presents a guided wave based damage localization framework using a time-space analysis for structural health monitoring of X-COR sandwich composites with a reference-free perspective to overcome the difficulty in detecting reflected guided waves in a highly attenuated media. Transducers, including macro-fiber composites and piezoelectric wafers, are used to design the sensing paths. The time-space domain is constructed using de-noised signals that are processed by signal processing techniques including matching pursuit decomposition and Hilbert transform. The localization framework is then validated across a wide range of excitation frequencies in X-COR sandwich composites with seeded facesheet delamination. The results indicate that time-space analysis offers a high accuracy for detection and localization of internal damages and serves as a promising framework for structural health monitoring of complex sandwich composites with reinforcements. This work also provides a comprehensive study of the changes in group velocities, attenuation tendencies, and time-space resolution of actuated and converted modes under different excitation frequencies across a range of ultrasonic transducer sizes, thereby helping to improve reliability and accuracy of damage localization in time-space domain.

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