Monitoring of the crack propagation in welded joint of the tank using multi-class recognition

• Autorzy: Bouraou N. , Rupich S. , Lukianchenko O. , Kostina O.
• Języki publikacji: EN

Abstrakty

EN

The numerical analysis of the vertical weld-fabricated steel tank is carried out taking into account the defects of the welds in the form of through-the-thickness cracks of different lengths and numbers, which are located in different zones of the object's ring. The influence of defects on the stress of the tank is estimated in the places of sensors installation under the action of vertical load. The usage of multi-class recognition is proposed by classifier based on Probabilistic Neural Network for monitoring of the crack propagation. Multidimensional vectors of diagnostic features are used for multi-class recognition. The training set of vectors is formed for defect-free and defect conditions, the classifier is trained and tested, the analysis of recognition efficiency is carried out by using the probability of correct multi-class recognition.

Słowa kluczowe

EN

welded tank; stress; crack propagation; multi-class recognition; neural network classifier

PL

zbiorniki spawane; naprężenie; propagacja pęknięć; sieci neuronowe jako klasyfikatory

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2018
• Tom: Vol. 29
• Strony: art. no. 2018013
• Opis fizyczny: Bibliogr. 8 poz., 1 il. kolor., 1 rys., 1 wykr.

Twórcy

autor

Bouraou N.

• National Technical University of Ukraine " Igor Sikorsky Kyiv Polytechnic Institute", 37 Peremogy Pr., Kyiv, Ukraine, 03056

autor

Rupich S.

• National Technical University of Ukraine " Igor Sikorsky Kyiv Polytechnic Institute", 37 Peremogy Pr., Kyiv, Ukraine, 030562

autor

Lukianchenko O.

• Kyiv National University of Construction and Architecture 31 Povitroflotsky Pr., Kyiv, Ukraine, 03680

autor

Kostina O.

• Kyiv National University of Construction and Architecture 31 Povitroflotsky Pr., Kyiv, Ukraine, 0368

Bibliografia

• 1. D. Adams, Health Monitoring of Structural Materials and Components. Methods with Applications, John Wiley & Sons Ltd., 2007.
• 2. D. Balageas, C. P. Fritzen, A. Gemes, Structural Health Monitoring, John Wiley & Sons Ltd., 2006.
• 3. S. Ignatovich, Probabilistic model of multiple-site fatigue damage of riveting in airframes, Strength of Materials, 46, 3 (2014) 336 - 344.
• 4. N. Bouraou, O. Lukianchenko, S. Tsybulnik, D. Shevchuk, Vibration condition monitoring of the vertical steel tanks, Vibrations in Physical Systems, 27 (2016) 55 - 60.
• 5. N. Bouraou, S. Tsybulnik, D. Shevchuk, The investigation of model of the vibration measuring channel of the complex monitoring system of vertical steel tanks, EEJET, 5/9 (2015) 45 - 52.
• 6. T. Shen, F. Wan, B. Song, Y. Wu, Damage location and identification of the wing structure with Probabilistic Neural Network, Proc. of Prognostics and System Health Management Conf., IEEE Xplore Digital Library 2011.
• 7. N. Bouraou, D. Pivtorak, S. Rupich, Multi-class recognition of objects technical condition by classifier based on Probabilistic Neural Network, EEJET, 5/4 (2017) 24 - 30.
• 8. D. F. Specht, Probabilistic Neural Networks, Neural Networks, 3 (1990) 109.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).