{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T14:49:48Z","timestamp":1763563788023,"version":"build-2065373602"},"reference-count":7,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2011,2,24]],"date-time":"2011-02-24T00:00:00Z","timestamp":1298505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019t 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).<\/jats:p>","DOI":"10.3390\/s110302334","type":"journal-article","created":{"date-parts":[[2011,2,25]],"date-time":"2011-02-25T01:55:21Z","timestamp":1298598921000},"page":"2334-2346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Non Destructive Defect Detection by Spectral Density Analysis"],"prefix":"10.3390","volume":"11","author":[{"given":"Ondrej","family":"Krejcar","sequence":"first","affiliation":[{"name":"Department of Measurement and Control, CAK, FEECS, VSB Technical University of Ostrava, Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Frischer","sequence":"additional","affiliation":[{"name":"Department of Automation and Computing in Metallurgy, VSB Technical University of Ostrava, Ostrava, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2011,2,24]]},"reference":[{"key":"ref_1","first-page":"676","article-title":"Defect diagnostics in devices via acoustic emission","volume":"11","author":"Bogorosh","year":"2009","journal-title":"J. Vibroeng"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1115\/1.4000480","article-title":"Defect diagnosis for rolling element bearings using acoustic emission","volume":"131","author":"He","year":"2009","journal-title":"J. Vib. Acoust"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11012-007-9101-7","article-title":"Critical defect size distributions in concrete structures detected by the acoustic emission technique","volume":"43","author":"Carpinteri","year":"2008","journal-title":"MECCANICA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1134\/S106183090604005X","article-title":"Acoustic emission used for detection of crack generation in propellers of turbine-pump units","volume":"42","author":"Volkovas","year":"2006","journal-title":"Russ. J. Nondestruct. Test"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Krejcar, O, and Frischer, R (2010, January 1\u20133). Material Inner Defect Detection by a Vibration Spectrum Analysis. Kyoto, Japan.","DOI":"10.1109\/ICMEE.2010.5558523"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1163\/156856108X331742","article-title":"Characteristics of acoustic emission signal specific to isolated cracks triggered by scratch tests in stainless steel coatings","volume":"22","author":"Gheorghies","year":"2008","journal-title":"J. Adhes. Sci. Technol"},{"key":"ref_7","unstructured":"Steel Prices (US$\/TON). Available online: http:\/\/www.airproducts.com\/metals_newsletter\/metal3-SteelPrices.htm (accessed on 25 August 2010)."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/3\/2334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:55:19Z","timestamp":1760219719000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/11\/3\/2334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,2,24]]},"references-count":7,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2011,3]]}},"alternative-id":["s110302334"],"URL":"https:\/\/doi.org\/10.3390\/s110302334","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2011,2,24]]}}}