Structural Health Monitoring of Wind Turbine Blades: Acoustic Source Localization Using Wireless Sensor Networks

Bouzid, Omar Mabrok; Tian, Gui Yun; Cumanan, Kanapathippillai; Moore, David G.

doi:10.1155/2015/139695

Cited by 53 publications

(26 citation statements)

References 26 publications

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“…Becker et al developed an autonomous WSN that employed crack wires and strain gauge sensors to monitor the health status of aircraft structures. Bouzid et al developed a wireless SHM system for the localisation of AE sources on wind turbine blades. The authors used the triangulation technique with three wireless‐driven surface‐bonded PZT transducers.…”

Section: Introductionmentioning

confidence: 99%

Low‐power global navigation satellite system‐enabled wireless sensor network for acoustic emission localisation in aerospace components

Giannì

Balsi

Esposito³

et al. 2020

Struct Control Health Monit

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Summary Structural health monitoring systems for the localisation of acoustic emission (AE) events have been developed in the past few years for aircraft components. However, these systems still require complex and heavy electrical wiring for each sensing device and sophisticated algorithms for the localisation of AE signals, which inevitably increases both weight and costs. This paper reports the creation of a low‐power (few hundred mW) and low‐weight (~30 g) global navigation satellite system‐based wireless sensor network for the identification of AE events in aircraft structures. Each wireless node is instrumented with a low‐power micro‐controller, a radio frequency wireless transceiver, an analogue‐to‐digital converter, and a global navigation satellite system receiver for accurate time synchronisation. The AE coordinates and the speed of propagating waves are determined by using a localisation algorithm relying on time of arrival measurements. A peak amplitude detection system is implemented to process the AE data recorded by the wireless sensor network. The AE time features are locally extracted by the individual wireless modules and transferred to a remote processing unit for the execution of the localisation algorithm. Experimental results revealed that AE sources generated by low‐velocity impacts are identified with a high level of accuracy. The successful implementation of the proposed wireless sensor network demonstrates its suitability for continuous and autonomous structural health monitoring aerospace applications.

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Section: Introductionmentioning

confidence: 99%

Low‐power global navigation satellite system‐enabled wireless sensor network for acoustic emission localisation in aerospace components

Giannì

Balsi

Esposito³

et al. 2020

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…In this study, Time correlated fatigue damage (89.82%), Mexican hat wavelet (79.23%), Meyer wavelet (79.76%), Daubechies 30th order (80.81%), Morlet wavelet (80.34%) and Discrete Meyer wavelet (80.30%) was used for the classification of crack on the blade. Bouzid et al [Bouzid, Tian, Cumanan et al (2015)] done a work on structural health monitoring of wind turbine blades using acoustic source localization and wireless sensor networks and obtained an error rate of 7.98% in their work. Liu et al [Liu, Jiang and Chu (2015)] carried out a study on the influence of alternating loads on nonlinear vibration characteristics of cracked blade in a rotor system using FEM analysis.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Joshuva¹,

Sugumaran²

2019

Structural Durability &Amp; Health Monitoring

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Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer. The statistical, histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.

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“…A work on structural health monitoring of wind turbine blades with acoustic source localization using wireless sensor networks was done by Bouzid et al, [10]. "They done the online monitoring of the blade for the fault identification of erosion and crack which affects the blade using acoustic emission NDT.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Blades using Histogram Features through Nested Dichotomy Classifiers

2019

IJRTE

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This study makes an attempt of classifying different fault conditions which occurs on wind turbine blade due to environmental stress and high wind speed. “Here three bladed horizontal axis variable wind turbine was used for experimental study and different faults like blade crack, hub-blade loose connection, erosion, pitch angle twist and blade bend was considered. This study had been carried out in three phases namely feature extraction, feature selection and feature classification. Initially vibration signals are noted for different blade conditions and required features are obtained using histogram features. Secondly, from the extracted feature, most dominating feature need to be chosen using J48 decision tree classifier. Later, the selected feature is fed into the classifiers like Nested Dichotomy (ND), Class-Balanced Nested Dichotomies (CBND) and Data near Balanced Nested Dichotomy (DNBND) for classification of the faults. These classifiers are compared with respect to their accuracy to suggest a better model for fault diagnosis on blade. The suggested model can be incorporated in real-time system to monitor the condition of wind turbine blade.

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Structural Health Monitoring of Wind Turbine Blades: Acoustic Source Localization Using Wireless Sensor Networks

Cited by 53 publications

References 26 publications

Low‐power global navigation satellite system‐enabled wireless sensor network for acoustic emission localisation in aerospace components

Low‐power global navigation satellite system‐enabled wireless sensor network for acoustic emission localisation in aerospace components

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Fault Diagnosis of Wind Turbine Blades using Histogram Features through Nested Dichotomy Classifiers

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