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Local fault detectionof rolling element bearing components by spectrogram clustering with Semi-Binary NMF
Jacek Wodecki 1  
,   Rafał Zdunek 2  
,   Agnieszka Wyłomańska 3  
,   Radosław Zimroz 1  
 
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1
Machinery Systems Division, Wroclaw University of Science and Technology, Poland
2
Faculty of Electronics, Wroclaw University of Science and Technology, Poland
3
KGHM CUPRUM Ltd CBR, Wrocław
CORRESPONDING AUTHOR
Jacek Wodecki   

Machinery Systems Division, Wroclaw University of Science and Technology, Poland, Wejherowska 31/30, 54-239 WROCLAW, Polska
Submission date: 2016-11-17
Final revision date: 2017-01-13
Acceptance date: 2017-01-13
Publication date: 2017-03-23
 
Diagnostyka 2017;18(1):3–8
 
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ABSTRACT
Information extraction is a very important problem nowadays. In diagnostics, it is particularly useful when one desires to isolate information about machine damage from a measured diagnostic signal. The method presented in this paper utilizes the idea that is based on a very important topic in numerical algebra, which is nonnegative matrix factorization. When applied to the matrix of multidimensional representation of the measured data, it can extract very useful information about the events which occur in the signal and are not recognizable otherwise. In the presented methodology, we use the algorithm called Semi-Binary Nonnegative Matrix Factorization (SB-NMF), and apply it to a time-frequency representation of the real-life vibration signal measured on faulty bearing operating in a belt conveyor driving station. Detected impulses of local damage are clearly identifiable. Performance of the algorithm is very satisfying in terms of time efficiency and output signal quality.
eISSN:2449-5220
ISSN:1641-6414