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Monitoring of fatigue crack growth in riveted aircraft structures by acoustic emission
Chlada, Milan ; Převorovský, Zdeněk
In principle, the acoustic emission (AE) represents one of NDT and structure health monitoring (SHM) methods for the detection and identification of growing material defects. Different types of signals detected by the AE method are supposed to correspond to different types of defects or operational noises. Monitoring of structures by acoustic emission needs new robust and fast methods for emission source location and classification. Recently proposed AE source location method using so called signal arrival time profiles and artificial neural networks (ANN) was applied for monitoring of growing defects during long term fatigue testing of riveted aircraft wing flange. The potentialities of the method regarding the on-line monitoring of dangerous crack growth in selected critical parts of aircraft structures are discussed.
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Structural Health Monitoring in aerospace and civil engineering supported with two ultrasonic NDT methods - AE and NEWS
Převorovský, Zdeněk ; Krofta, Josef ; Farová, Zuzana ; Chlada, Milan
Structural Health Monitoring (SHM) becomes today an important technology improving reliability and safety of aeronautical and civil structures. Combining of two ultrasonic NDT methods - Acoustic Emission (AE) with Nonlinear Elastic Wave Spectroscopy (NEWS) - in SHM systems brings many advantages. AE enables real-time detection and localization of initiation and progression under operational stimulations, and NEWS methods provide retrieval of structural faults and complete information on AE sources. Examples of a complex SHM system design for critical parts of both aircraft and building roofs are discussed in this paper. The NEWS pseudo-tomography procedure, based on Time Reversal Mirrors (TRM) with Excitation Symmetry Analysis Method (ESAM), enabled zone-location of initiating defects, not detectable by other classical NDT procedures.Our results reflect good robustness of both techniques, and support their helpfulness in SHM of aircraft and building structures.
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Detection of AE Sources During Long-term Fatigue Tests of Riveted Aircraft Wing Flange
Chlada, Milan ; Převorovský, Zdeněk
Fracture is the primary threat to the integrity, safety, and performance of nearly all highly stressed mechanical structures, e.g., aircrafts, building units or pressure vessels. Contemporary exacting demands on reliability and safety of material structures are not realizable without effective means of NDT and continuous state monitoring. AE monitoring of structures needs new robust and fast methods for emission source location and classification. Recently proposed AE source location method using so-called signal arrival time profiles and artificial neural networks (ANN) was applied for monitoring of growing defects during long-term fatigue testing of riveted aircraft wing flange. The potentialities of the method regarding the on-line monitoring of dangerous crack growth in selected critical parts of aircraft structures are discussed.
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AE source location by neural networks independent on material scale changes
Chlada, Milan ; Převorovský, Zdeněk
The localization of acoustic emission (AE) sources by procedures using artificial neural networks (ANN) represents today highly effective alternative approach to classical triangulation algorithms. The main problems are in the collecting sufficiently extensive training and testing data sets together with the non portability of particular trained network to any other object. Recently, the ANN based AE source location method has been improved by using so-called signal arrival time profiles to overcome both limitations. This way of signal arrival time characterization enables ANN training on numerical models and allows the application of learned ANN on real structures of various scales and materials. In this paper, the method is upgraded and localization results are illustrated on experimental data obtained during pen-tests on a model roof I-beam and an aircraft structure part. General application possibilities of the method variations for different sensor configurations are also discussed.
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Application of Arrival Time Profiles to AE Source Location by Neural Networks
Chlada, Milan ; Blaháček, Michal ; Převorovský, Zdeněk
The localization procedures using artificial neural networks (ANN) represent today highly effective, alternative approach to classical triangulation algorithms. Nevertheless, their application possibilities are limited due to several reasons. The main problems are in the collecting of sufficiently extensive training and testing data sets together with the non-portability of particular trained network to any other object. In recent time, a new ANN-based AE source location method using so-called signal arrival time profiles was proposed to overcome both limitations. The new way of signal arrival time characterization provides the ANN training on numerical models and allows the application of learned ANN on real structures of various scales and materials. In the paper, this new method is illustrated on experimental data obtained at complex aircraft structure part testing, and its remarkable advantages concerning the considerable extension of ANN application possibilities are discussed.
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Lokalizace zdrojů akustické emise pomocí neuronových sítí na základě časových profilů
Chlada, Milan ; Blaháček, Michal ; Převorovský, Zdeněk
Correct localization of acoustic emission (AE) sources is a basic requirement in AE analysis and consequent evaluation of damage mechanism. The localization procedures using artificial neural networks (ANN) represent today highly effective, alternative approach to classical triangulation algorithms. Nevertheless, their application possibilities are limited due to problematic collecting of sufficiently extensive training and testing data sets together with the non-portability of particular trained network to any other object. A new ANN-based approach, using so-called signal arrival time profiles, is proposed to overcome both limitations. Such approach provides the ANN training on numerical models and allows the application of learned ANN on real structures of various scales and materials. This enables considerable extension of ANN application possibilities. New method is illustrated on experimental data obtained during pen-tests on a steel plate, and its remarkable advantages are discussed.
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NONDESTRUCTIVE EVALUATION OF CONCRETE STRUCTURES BY NONLINEAR ULTRASONIC SPECTROSCOPY METHODS
Převorovský, Zdeněk ; Krofta, Josef ; Chlada, Milan
Facilities of nonlinear ultrasonic spectroscopy for defects detection in steel-reinforced concrete structures are discussed in the paper. Results are shown from non-destructive evaluation of concrete block samples reinforced with intact and corroded armature. Seven piezoelectric transducers were glued on the sample surface. One of them was used to harmonic excitation of the structure with frequency f1=45 kHz and stepwise growing amplitude. Among other 6 transducers one was multiplexed as a second transmitter of harmonic signal with f2=217 kHz and remaining sensors were receiving structural response on 2-frequency excitation. Most pronounced spectral differences between samples with corroded and intact reinforcement were observed on amplitude dependence of 3f1/2f1 ratio and f2-3f1 sidebands, which can be considered as nonlinear parameters characterizing concrete damage.
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OPTIMIZED NUMBER OF SIGNAL FEATURES FOR IDENTIFICATION OF AE SOURCES
Chlada, Milan ; Převorovský, Zdeněk
Artificial neural networks (ANN) are effective instruments for identification of AE sources. The proper selection of extracted data features is complicated task in general data recognition. Standard AE signal parameters are often redundant or not relevant in recognition problem. Modifications of standard AE signal features are proposed in this paper as to reduce data redundancy. Set of extracted AE parameters is optimized by factor analysis and sensitivity analysis of recognizing neural networks. This optimization is illustrated by recognition of AE sources arising during fatigue tests performed on aircraft structure parts. Optimized AE signal features cover enough information with minimized number of parameters.
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Expertní detekce příchodu signálu AE
Chlada, Milan
Accurate acoustic emission (AE) source location is the primary goal of the defect anylysis following the AE signal detection. The source localization is mostly based on arrival time differences of signals recorded by several transducers. Considerable signal distortion happens during the wave propagation through the solid. Inaccurate determination of signal onset and arrival time differences respectively, are the greatest sources of localization errors.Especially, in a case of figher requirements on accuracy and robustness, the results of currently used localization methods appear to be insufficient. In the paper, recently improved version of the new signal-shape based algorithm, modelling an expert system of the elastic wave arrival detection, is introduced. In many applications, this method, based on signal energy and local gravity center evolution, has been proved as rugged enough, fast and easily applicable.
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