Open AccessDetection of bearing damage by statistic vibration analysis
Author(s) -
Evgeny Alexandrovich Sikora
Publication year - 2016
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/124/1/012167
Subject(s) - bearing (navigation) , vibration , noise (video) , signal (programming language) , fault (geology) , acoustics , computer science , structural engineering , filter (signal processing) , gaussian noise , engineering , artificial intelligence , physics , geology , computer vision , seismology , image (mathematics) , programming language
The condition of bearings, which are essential components in mechanisms, is crucial to safety. The analysis of the bearing vibration signal, which is always contaminated by certain types of noise, is a very important standard for mechanical condition diagnosis of the bearing and mechanical failure phenomenon. In this paper the method of rolling bearing fault detection by statistical analysis of vibration is proposed to filter out Gaussian noise contained in a raw vibration signal. The results of experiments show that the vibration signal can be significantly enhanced by application of the proposed method. Besides, the proposed method is used to analyse real acoustic signals of a bearing with inner race and outer race faults, respectively. The values of attributes are determined according to the degree of the fault. The results confirm that the periods between the transients, which represent bearing fault characteristics, can be successfully detected