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2 Zeszyty Naukowe Politechniki Rzeszowskiej. Budownictwo i Inżynieria Środowiska

2 2009

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Fale sprężyste w badaniach konstrukcji. Cz.2. Doświadczenia laboratoryjne

Nazarko P., Jurek M., Ziemiański L.

Zeszyty Naukowe Politechniki Rzeszowskiej. Budownictwo i Inżynieria Środowiska

2009

z. 55 [267]

43-50

The paper presents an idea of elastic waves application in the field of structure test and health monitoring. Laboratory experiments were performed for several laboratory specimens made of various materials. In this paper strips elements and plates were analyzed. The main components of the used equipment and the laboratory setup were discussed. The obtained results of preliminary tests have shown that such the approach can be successfully used for the purpose of diagnosis systems since it provides good indication about damage appearance and predicts its size with reasonably well accuracy.

Fale sprężyste w badaniach konstrukcji. Cz.1. Przetwarzanie sygnałów

Nazarko P., Jurek M., Ziemiański L.

Zeszyty Naukowe Politechniki Rzeszowskiej. Budownictwo i Inżynieria Środowiska

2009

z. 55 [267]

37-42

The paper presents an idea of elastic waves application in the field of structure test and health monitoring. Smart technology used for this purpose can lead further to autonomous systems that may operate in real time providing information about the structure state or even remaining operational life. However, the analysis of the elastic waves signals, assuming reflections from structure boundaries, connections, cracks, delaminations, etc., may be rather clear or pretty complex. Due to this fact advance signal processing techniques were used here for a purpose of signal de-noising and features extraction. The proposed system performs two levels of structure diagnosis: novelty detection and damage prediction. The developed procedure of signal processing has been studied for the elastic waves signals measured in various laboratory specimens. It has been proved that the application of those techniques improves the accuracy of the designed diagnosis system. Trained neural networks were able to detect damage and predict its size with reasonably well accuracy.