Acoustic emission detection of early stages of cracks in rotating gearbox components
Author(s) -
Dan Xiang
Publication year - 2017
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.4974625
Subject(s) - acoustic emission , signal (programming language) , acoustics , attenuation , amplitude , feature extraction , energy (signal processing) , feature (linguistics) , signal processing , computer science , pattern recognition (psychology) , artificial intelligence , physics , telecommunications , optics , linguistics , philosophy , radar , quantum mechanics , programming language
Many critical, highly loaded rotating gearbox components have fast crack propagation rates. Early detection of cracks in gearbox is critical to mitigating the risk of catastrophic failure. Acoustic Emission (AE) techniques have proven to be capable of continuously monitoring the crack initiation and propagation. Due to the long distance of AE signal propagation from the AE sources to the sensors installed in the housing, the AE signal suffers from severe attenuation and noises. Accurate AE signal classification technology that is capable of extracting the true AE signal out of background noises generated by the surrounding environment of a gearbox is desired. In this paper, an innovative feature extraction and analysis based AE signal classification technology is developed to address this issue. Potential AE signals are first pulled out of the noisy background in real-time through a set of automated AE detection algorithms. Then features including count, energy, duration, amplitude, rise time, amplitude r...
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