Automated Damage Detection in Composite Components Using Acoustic Emission

2013 ◽  
Vol 569-570 ◽  
pp. 80-87 ◽  
Author(s):  
Rhys Pullin ◽  
Matthew R. Pearson ◽  
Mark J. Eaton ◽  
Carol A. Featherston ◽  
Karen M. Holford ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Composite Structures ◽  
Impact Damage ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Potential Method ◽  
Base Line ◽  
Wind Turbine Blades ◽  
Ae Signals

The ability of a Structural Health Monitoring (SHM) system to automatically identify damage in a composite structure is a vital requirement demanded by end-users of such systems. This paper presents the demonstration of a potential method. A composite fatigue specimen was manufactured and initially tested at 1Hz for 1000 cycles. Acoustic emission (AE) signals were recorded for complete fatigue cycles periodically in order to establish a base-line associated with undamaged specimens. The specimen was then subjected to impact damage to create barely-visible impact damage (BVID) and subjected to further fatigue cycles with acoustic emission recorded until failure. The data was subsequently analysed using a range of techniques including basic RMS signal levels and frequency-based analysis. At various stages during the test, C-scanning was used to validate the results obtained. Results demonstrated that AE is capable of detecting BVID in composite materials under fatigue loading. The proposed method has wide applicability to composite structures which are subjected to cyclic loading, such as wind turbine blades.

scholarly journals Detection of Cracking in Gear Teeth Using Acoustic Emission

2010 ◽  
Vol 24-25 ◽  
pp. 45-50 ◽  
Author(s):  
Rhys Pullin ◽  
A. Clarke ◽  
Mark J. Eaton ◽  
Karen M. Holford ◽  
S.L. Evans ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Gear Tooth ◽  
Fatigue Loading ◽  
Image Correlation ◽  
Initial Development ◽  
Gear Teeth ◽  
Future Work ◽  
Inspection Techniques ◽  
Ae Signals

The detection of damage in gear teeth is paramount to any condition monitoring or structural health monitoring (SHM) tool for aerospace power transmissions such as those used in helicopters. Current inspection techniques include vibration analysis and time-inefficient visual inspection. Acoustic Emission (AE) is a very sensitive detection tool that has been successfully used in many SHM systems. Successful application of AE for damage detection in gear teeth will enable the optimisation of gear box design (and hence weight saving) in addition to safety improvements. This paper details a small aspect of a larger project designed to demonstrate automatic detection and location of common gear tooth defects. A novel test rig was designed to allow the fatigue loading of an individual gear tooth which was monitored using AE. The gear tooth was static in order to exclude the detection of AE signals arising from rotation; this allows initial development of the methodology prior to investigating rotating gears. Digital Image Correlation was used to determine the onset of cracking for comparison with the detected AE. Preliminary results of the investigation show that the developed methodology is appropriate for developing an automated gear health monitoring system and that future work should concentrate on the development of sensors and data acquisition methods associated with obtaining signals from rotating machinery.

Health monitoring of operating wind turbine blades with acoustic emission

2012 ◽  
pp. 347-352 ◽  
Author(s):  
N Tsopelas ◽  
D Kourousis ◽  
I Ladis ◽  
A Anastasopoulos ◽  
D Lekou ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Wind Turbine Blades

scholarly journals Impact Damage Ascertainment in Composite Plates Using In-Situ Acoustic Emission Signal Signature Identification

2021 ◽  
Vol 5 (3) ◽  
pp. 79
Author(s):  
Robin James ◽  
Roshan Prakash Joseph ◽  
Victor Giurgiutiu
Keyword(s):  
Acoustic Emission ◽  
Health Monitoring ◽  
Impact Damage ◽  
Impact Event ◽  
Internal Damage ◽  
Emission Signal ◽  
Monitoring Technique ◽  
The Impact ◽  
Impact Events ◽  
Ae Signals

Barely visible impact damage (BVID) due to low velocity impact events in composite aircraft structures are becoming prevalent. BVID can have an adverse effect on the strength and safety of the structure. During aircraft inspections it can be extremely difficult to visually detect BVID. Moreover, it is also a challenge to ascertain if the BVID has in-fact caused internal damage to the structure or not. This paper describes a method to ascertain whether or not internal damage happened during the impact event by analyzing the high-frequency information contained in the recorded acoustic emission signal signature. Multiple 2 mm quasi-isotropic carbon fiber reinforced polymer (CFRP) composite coupons were impacted using the ASTM D7136 standard in a drop weight impact testing machine to determine the mass, height and energy parameters to obtain approximately 1” impact damage size in the coupons iteratively. For subsequent impact tests, four piezoelectric wafer active sensors (PWAS) were bonded at specific locations on each coupon to record the acoustic emission (AE) signals during the impact event using the MISTRAS micro-II digital AE system. Impact tests were conducted on these instrumented 2 mm coupons using previously calculated energies that would create either no damage or 1” impact damage in the coupons. The obtained AE waveforms and their frequency spectrums were analyzed to distinguish between different AE signatures. From the analysis of the recorded AE signals, it was verified if the structure had indeed been damaged due to the impact event or not. Using our proposed structural health monitoring technique, it could be possible to rapidly identify impact events that cause damage to the structure in real-time and distinguish them from impact events that do not cause damage to the structure. An invention disclosure describing our acoustic emission structural health monitoring technique has been filed and is in the process of becoming a provisional patent.

Geometrically exact, intrinsic mixed variational formulation for smart, slender multilink composite structures

2021 ◽  
pp. 1045389X2110322
Author(s):  
P M G Bashir Asdaque ◽  
Sitikantha Roy
Keyword(s):  
Composite Structures ◽  
Turbine Blades ◽  
Space Structures ◽  
Wind Turbine Blades ◽  
Weak Formulation ◽  
Helicopter Blades ◽  
Static Mechanical ◽  
Computational Platform ◽  
Swept Wings ◽  
Variational Statement

Flexible links are often part of massive aerospace structures like helicopter or wind turbine blades, satellite bae, airplane wings, and space stations. In the present work, a mixed variational statement based on intrinsic variables is derived for multilinked smart slender structures. Equations involved in the derivation do not involve approximations of kinematical variables to describe the deformation of the reference line or the rotation of the deformed cross-section of the slender links resulting in a geometrically exact formulation. Finite element equations are derived from weak formulation, which can analyze large geometrically non-linear problems. The weakest possible variational statement provides greater flexibility in the choice of shape functions, therefore reducing the associated numerical complexities. The present work focuses on developing a single integrated computational platform which can study multibody, multilink, lightweight composite, structural system built with both embedded actuations, sensing, as well as passive links. Validation of static mechanical and electrical outputs from 3D FE simulation and literature proves the efficacy of the computational platform. Dynamic results will be communicated in future correspondence. The computational platform developed here can be applied for monitoring and active control applications of flexible smart multilink structures like swept wings, multi-bae space structures, and helicopter blades.

Structural Health Monitoring of Research-Scale Wind Turbine Blades

2013 ◽  
Vol 558 ◽  
pp. 364-373 ◽  
Author(s):  
Stuart G. Taylor ◽  
Kevin M. Farinholt ◽  
Gyu Hae Park ◽  
Charles R. Farrar ◽  
Michael D. Todd ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Embedded Systems ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Field Tests ◽  
Ultrasonic Waves ◽  
Wind Turbine Blades ◽  
Structural Health ◽  
Sensing System

This paper presents ongoing work by the authors to implement real-time structural health monitoring (SHM) systems for operational research-scale wind turbine blades. The authors have been investigating and assessing the performance of several techniques for SHM of wind turbine blades using piezoelectric active sensors. Following a series of laboratory vibration and fatigue tests, these techniques are being implemented using embedded systems developed by the authors. These embedded systems are being deployed on operating wind turbine platforms, including a 20-meter rotor diameter turbine, located in Bushland, TX, and a 4.5-meter rotor diameter turbine, located in Los Alamos, NM. The SHM approach includes measurements over multiple frequency ranges, in which diffuse ultrasonic waves are excited and recorded using an active sensing system, and the blades global ambient vibration response is recorded using a passive sensing system. These dual measurement types provide a means of correlating the effect of potential damage to changes in the global structural behavior of the blade. In order to provide a backdrop for the sensors and systems currently installed in the field, recent damage detection results for laboratory-based wind turbine blade experiments are reviewed. Our recent and ongoing experimental platforms for field tests are described, and experimental results from these field tests are presented. LA-UR-12-24691.

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.

Acoustic Emission for Structural Integrity Assessment of Wind Turbine Blades

2014 ◽  
pp. 369-382 ◽  
Author(s):  
Nikolaos K. Tsopelas ◽  
Dimitrios G. Papasalouros ◽  
Athanasios A. Anastasopoulos ◽  
Dimitrios A. Kourousis ◽  
Jason W. Dong
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Structural Integrity ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Integrity Assessment

RESEARCH ON THE SELF-SYNCHRONOUS VIBRATION OF FATIGUE LOADING TESTS FOR WIND TURBINE BLADES

2016 ◽  
Vol 40 (5) ◽  
pp. 871-881
Author(s):  
Huang Xuemei ◽  
Zhang Lei’an ◽  
Tao Liming ◽  
Wei Xiuting
Keyword(s):  
Wind Turbine ◽  
Initial Phase ◽  
Turbine Blades ◽  
Fatigue Loading ◽  
Test System ◽  
The Self ◽  
Driving Frequency ◽  
Wind Turbine Blades ◽  
Loading System ◽  
Loading Tests

To carry on fatigue loading tests for wind turbine blades accurately, the self-synchronous vibration mechanism of loading system was investigated. Firstly, the mathematical model of vibration was deduced based on LaGrange Equation, thus the influence factors of self-synchronous vibration could be obtained. Then to study the influencing rules of the initial phase difference between loading equipment and blade, a simulating model was constructed to carry on the numerical simulation and it was found that when the driving frequency of the loading equipment was the same as the natural frequency of the blade, a different initial phase separation would generate different effect on self-synchronous vibration. Finally, an on-site fatigue test system was established to verify the accuracy of mathematical and simulation model mentioned above. It could be concluded that the test results were consistent with the simulating result. The research on the self-synchronous vibration performance of loading system for blade could supply a theory support for the sequent control of blade’s fatigue tests precisely.

Acoustic Emission Monitoring of Small Wind Turbine Blades

2002 ◽  
Author(s):  
P. A. Joosse ◽  
M. J. Blanch ◽  
A. G. Dutton ◽  
D. A. Kouroussis ◽  
T. P. Philippidis ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Fibre Composite ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Static Test ◽  
Damage Propagation ◽  
Test Methodology ◽  
Certification Procedure ◽  
Damage Processes

Wind turbine blade certification tests, comprising a static test, a fatigue test, and finally a residual strength test, often involve sudden audible cracking sounds from somewhere within the blade, without the operators being able to locate the noise source, or to determine whether damage (minor or major) has occurred. A current EC-funded research project is looking at the possibility of using acoustic emission (AE) monitoring during testing of fibre composite blades to detect such events and assess the blade condition. AE can both locate and characterise damage processes in blades, starting with non-audible signals occurring due to damage propagation at relatively low loads. The test methodology is discussed in the context of the blade certification procedure and results are presented from a series of static and fatigue blade tests to failure in the laboratory. Inferences are drawn about small differences in the manufacture of the nominally identical blades and conclusions are presented for the application of the methodology.

Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms

2016 ◽  
Author(s):  
Taylor Regan ◽  
Rukiye Canturk ◽  
Elizabeth Slavkovsky ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

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