TIME DOMAIN BEARING DEFECTS DIAGNOSIS BASED ON GAUSSIAN FILTERING AND FUZZY LOGIC

Bogdan BETEA, Petru DOBRA

Abstract


On this paper a novel bearing defects diagnosis method is introduced. This diagnosis method is based on time domain signal processing first step being the Gaussian filtering.

The reason of this filtering is the detection of the shock pulses generated by bearing defects. The diagnosis is done based one the detected defects periods which are used as inputs for a fuzzy classifier that provide defect alerts for each kind of bearing localize defect.

Key words: bearing defects, Gaussian filtering, fuzzy classification.

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References


Gregory Goddu, Bo Li, Mo-Yuen Chow, James C. Hung, “Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system “Industrial Electronics Society, 1998. IECON '98. Proceedings of the 24th Annual Conference of the IEEE, 31 Aug-4 Sep 1998, 1961 - 1965 vol.4

Bogdan Betea, Mircea-Cristian Gherman, Monica Borda, Petru Dobra, ”Bearing Defects Signals Demodulation Using Shock Filters”, Acta Technica Napocensis Electronics and Telecommunications, Volume 53, Number 3/2012, pp.35-40

Bogdan Betea, Petru Dobra, Mircea-Cristian Gherman and Liviu Tomesc, “Comparison between envelope detection methods for bearing defects diagnose”, 2nd IFAC Workshop on Convergence of Informational Technologies and Control Methods with Power Systems ICPS'13, pp. 145-150

Lampert C. H., and Wirjadi O., (2006). Anisotropic Gaussian filtering using fixed point arithmetic, Image Processing – IEEE International Conference, pp. 1565 – 1568.

“Bearing Data Center (B.D.C.)” Website of Case Western Reserve University, Cleveland, Ohio, USA (2006)

http://www.geocities.ws/jaime_urzua/sciFLT/sciflt.html

https://www.scilab.org/


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