25 May 2004 Revealing, identifying, and assessing flaws in operating equipment by the acoustic emission image recognition method under strong background noise condition
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Proceedings Volume 5472, Noise and Information in Nanoelectronics, Sensors, and Standards II; (2004) https://doi.org/10.1117/12.547514
Event: Second International Symposium on Fluctuations and Noise, 2004, Maspalomas, Gran Canaria Island, Spain
Abstract
The analysis has shown that high pressure and high temperature piping in fossil and nuclear power plants suffer from unexpected and rarely predictable failures. To guarantee operational safety and to prevent failures authors have performed the complex investigations and have created Quantitative Acoustic Emission NDI technology for revealing, identifying and assessing flaws in equipment operated under strong background noise condition. These enabled: Overall inspection of the piping operated under stress, temperature, pressure, steam flow and loading, variation. Locating suspected zones and zones of flaw development with low J-integral value and the great variation of the dynamic range of flaws danger level. Identification of flaw types and their danger level. Detection of defective components in service prior to shut down. The continuous and the burst Acoustic Emission (AE) were used in combination as an information tool. As result, the significant number of flaws such as creep at stage 3a-3b, closed-edge micro-cracks, systems of randomly dispersed pores and inclusions, plastic deformation development around them, or/and individual micro-cracking were revealed, identified and assessed in 50 operating high energy piping. The findings and assessing flaw danger level obtained by QAE NDI were confirmed by independent NDI methods as TOFD, X-ray, replication, metallurgical investigations, etc. The findings and assessing flaw danger level obtained by QAE NDI were confirmed by independent NDI methods such as TOFD, X-ray, replication, metallurgical investigations, etc
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Gregory Muravin, Gregory Muravin, Boris Muravin, Boris Muravin, Ludmila Lezvisky, Ludmila Lezvisky, } "Revealing, identifying, and assessing flaws in operating equipment by the acoustic emission image recognition method under strong background noise condition", Proc. SPIE 5472, Noise and Information in Nanoelectronics, Sensors, and Standards II, (25 May 2004); doi: 10.1117/12.547514; https://doi.org/10.1117/12.547514
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