MATEC Web Conf.
Volume 63, 20162016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
|Number of page(s)||6|
|Section||Mechatronic and Application Engineering|
|Published online||12 July 2016|
Acoustical Semi-blind Deconvolution for Bearing Defect Detection
Xihua University, School of Mechanical Engineering, Chengdu, China
a Corresponding author: firstname.lastname@example.org
Acoustical machine monitoring is frequently complicated by noisy environments at a production site. This paper presents a semi-blind deconvolution algorithm to extract only one desired acoustic source signal from different sources which are convoluted and mixed by mechanical systems before being measured. The method is based on blind model transformation, robust independent component analysis, reference signal and spectral distance. The new algorithm is tested on simulation and experimental cases. Results demonstrate that blind separation of acoustic signals is possible even when measurements are distanced from vibration exciting sources of faulty bearings. Furthermore, the method can eliminate the effect of structural resonances and large reverberation time of mixtures, which often causes severe problems in classical acoustical diagnostic methods of rolling element bearings.
© Owned by the authors, published by EDP Sciences, 2016
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