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Acoustic emission nondestructive testing has been used for real-time monitoring of complex structures. All of the structures were made of materials at least 0.070 inch thick. The purpose of this research was to demonstrate the feasibility of using neural networks to classify acoustic emission signals gathered from a pressure vessel made of 2024-T3 aluminum 0.040 inches thick, i.e. thin aluminum sheet. AE waveforms were recorded during fatigue cycling of one pressure vessel using a wide band transducer and a digital oscilloscope connected to a computer. The source for each signal was determined using two narrow band transducers and a LOCAN-AT data acquisition system. The power spectrum was calculated for each waveform. A Kohonen self-organizing map (SOM) was used to cluster the spectra. The network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature space; however, this was found to be due to human error. The SOM itself correctly classified all of the signals.

Titel:	Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map
Autor/in / Beteiligte Person:	Thornton, Weldon Paul
Link:	View this record from OAIster (Volltext)
Veröffentlichung:	2023
Medientyp:	unknown
Umfang:	1 online resource (ix, 64 leaves) : illustrations
Schlagwort:	Acoustic emission testing. Self-organizing maps. Pressure vessels Testing. Aluminum. Contrôle par émission acoustique. Cartes auto-organisatrices. Aluminium. aluminum (metal) Acoustic emission testing Aluminum Pressure vessels Testing Self-organizing maps Subjects: Acoustic emission testing. Self-organizing maps. Pressure vessels Testing. Aluminum. Contrôle par émission acoustique. Cartes auto-organisatrices. Aluminium. aluminum (metal) Acoustic emission testing Aluminum Pressure vessels Testing Self-organizing maps Index Terms: Aerospace Engineering Structures and Materials
Sonstiges:	Nachgewiesen in: OAIster Added Details: Hill, Eric v. K., advisor ; Embry-Riddle Aeronautical University. Department of Aerospace Engineering. ; Eric v. K. Hill ; Frank Radosta ; Sathya Gangadharan ; Thornton, Weldon Paul Call Numbers: TA418.84 .T46 1995eb Availability: Open access content. Open access content Note: Also available in print. ; "Daytona Beach, Florida, Spring 1995." ; Includes bibliographical references (leaves 56-57). ; Copyright ; Signature page ; Acknowledgements ; Abstract ; Table of contents ; List of tables ; List of figures -- 1. Introduction. 1.1. General overview ; 1.2. Previous research ; 1.3. Current approach -- 2. Background theory. 2.1. Acoustic emission. 2.1.1. Phenomenology ; 2.1.2. Application to metal fatigue detection ; 2.1.3.Effects of dispersive mediums ; 2.2. Neural networks. 2.2.1. Neural network applications and learning methods ; 2.2.2. Neural network architecture ; 2.2.3. Previous research -- 3. Experimental apparatus and testing procedure. 3.1.Experimental apparatus. 3.1.1. Pressure vessel ; 3.1.2. Pressurization system ; 3.1.3. Sensors ; 3.1.4. Data acquisition ; 3.2. Data conditioning. 3.2.1. Preprocessing ; 3.2.2. Bandwidth requirements ; 3.3. Testing procedure -- 4. Neural network architecture, operation, and implementation. 4.1. Selection of a neural network. 4.1.1. Input data for the neural network ; 4.1.2. Justification for using a neural network ; 4.2. Architecture of neural network selected ; 4.3. Neural network operation ; 4.4. Neural network implementation. 4.4.1. Selection of control parameters ; 4.4.2. Training the neural network ; 4.4.3. Testing the neural network -- 5. Analysis and verification of results. 5.1. Preliminary networks and their results ; 5.2. Analysis and verification of results -- 6. Conclusions and recommendations. 6.1. Conclusions ; 6.2. Recommendations. Contributing Source: From OAIster®, provided by the OCLC Cooperative.

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Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map