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Location of acoustic emission sources in geometrically sparce structures
Chlada, Milan ; Převorovský, Zdeněk
In recent years, the importance of modern philosophy of Structural Health Monitoring (SHM) is increasing. SHM systems are gradually integrated into, e.g., aircraft or bridge structures and also become newly a part of certain modern buildings, where the break down would cause significant material or human losses. One of the effective prewarning methods is the detection of acoustic emission (AE). The paper yields the discussion about the utilization of artificial neural networks in comparison with the segment location based on the chronology of signal arrivals to selected groups of AE sensors for “geometrically sparse” structures. Both methods and their results are demonstrated in case of experiment with gradual loading of a part of a roof construction until the final breakdown.
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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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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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