Conveners
Acoustic Emission and Defectoscopy
- Václav Kůs (KM FJFI CVUT)
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Mr Milan Chlada6/20/25, 2:00 PM
This paper focuses on using machine learning methods to detect and monitor fatigue crack propagation using ultrasonic signals. The basic principle is the detection of a prolonged transit time of the signal as an indicator of changes in the material caused by growth of the failure. A multilayer perceptron (MLP) network supplemented with simple convolutional layers is employed to analyse the...
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Petr Vojtášek (Student)6/20/25, 2:25 PM
The research focuses on the classification of bearing damage using acoustic emission data. The methodology involves the collection of experimental data across six categories of bearing faults using three different types of sensors. Several neural network architectures are proposed and evaluated, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and...
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Martin Satranský (CTU FNSPE)6/20/25, 2:50 PM
As a part of an experiment, which is to be carried out at the Institute of Thermodynamics of the Czech Academy of Sciences, measuring acoustic emission in a bearing assembly the need rose to design and implement an application with specific requirements intended for long-term data collection from a TiePie oscilloscope. This is the first of two parts of my bachelor thesis, in which data from...
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Dr Zuzana Dvořáková (Institute of Thermomechanics of the CAS)6/20/25, 3:15 PM
Reliable localisation of acoustic emission (AE) sources is one of the most critical inverse problems in non-destructive testing and structural health monitoring of engineering structures. Conventional AE localisation techniques often fail when applied to complex structures exhibiting wave dispersion, velocity variations, or geometric irregularities. Locating continuous AE signals, such as...
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