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Sensors 2011, 11(3), 2334-2346;

Non Destructive Defect Detection by Spectral Density Analysis

Department of Measurement and Control, CAK, FEECS, VSB Technical University of Ostrava, Ostrava, Czech Republic
Department of Automation and Computing in Metallurgy, VSB Technical University of Ostrava, Ostrava, Czech Republic
Author to whom correspondence should be addressed.
Received: 3 January 2011 / Revised: 28 January 2011 / Accepted: 2 February 2011 / Published: 24 February 2011
(This article belongs to the Special Issue Advanced Sensing Technology for Nondestructive Evaluation)
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The potential nondestructive diagnostics of solid objects is discussed in this article. The whole process is accomplished by consecutive steps involving software analysis of the vibration power spectrum (eventually acoustic emissions) created during the normal operation of the diagnosed device or under unexpected situations. Another option is to create an artificial pulse, which can help us to determine the actual state of the diagnosed device. The main idea of this method is based on the analysis of the current power spectrum density of the received signal and its postprocessing in the Matlab environment with a following sample comparison in the Statistica software environment. The last step, which is comparison of samples, is the most important, because it is possible to determine the status of the examined object at a given time. Nowadays samples are compared only visually, but this method can’t produce good results. Further the presented filter can choose relevant data from a huge group of data, which originate from applying FFT (Fast Fourier Transform). On the other hand, using this approach they can be subjected to analysis with the assistance of a neural network. If correct and high-quality starting data are provided to the initial network, we are able to analyze other samples and state in which condition a certain object is. The success rate of this approximation, based on our testing of the solution, is now 85.7%. With further improvement of the filter, it could be even greater. Finally it is possible to detect defective conditions or upcoming limiting states of examined objects/materials by using only one device which contains HW and SW parts. This kind of detection can provide significant financial savings in certain cases (such as continuous casting of iron where it could save hundreds of thousands of USD). View Full-Text
Keywords: FFT; power spectrum; MatLab; Statistica; defect FFT; power spectrum; MatLab; Statistica; defect

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Krejcar, O.; Frischer, R. Non Destructive Defect Detection by Spectral Density Analysis. Sensors 2011, 11, 2334-2346.

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