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Use of wavelet transform and fuzzy system theory to distinguish wear particles in lubricating oil for bearing diagnosis
Author(s) -
Utsumi Shinji,
Kawasaki Zenichiro,
Matsuura Kenji,
Kawada Masatake
Publication year - 2000
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/1520-6416(20010115)134:1<36::aid-eej5>3.0.co;2-l
Subject(s) - gabor wavelet , gabor transform , bearing (navigation) , function (biology) , fuzzy logic , wavelet , artificial intelligence , wavelet transform , pattern recognition (psychology) , membership function , materials science , computer vision , time–frequency analysis , computer science , mathematics , discrete wavelet transform , fuzzy set , filter (signal processing) , evolutionary biology , biology
Ferrographic analysis is required in order to detect wear particles in lubricating oil automatically, because the customary approach takes a great deal of time. We propose a new method to detect wear particles in lubricating oil in order to diagnose bearings, by means of local spatial frequency analysis using the wavelet transform. The Gabor function and cylindrical Gabor function are used as the mother functions of the wavelet transform in this paper. The Gabor function is effective in detecting particles which distribute along the lines of magnetic force on the ferrogram slide. The cylindrical Gabor function can detect circular particles. To discriminate the particles, we apply fuzzy system theory to the image transformed by two Gabor functions and show the effectiveness of this method. © 2000 Scripta Technica, Electr Eng Jpn, 134(1): 36–44, 2001