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Title: Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery
Authors: Brković, Aleksandar
Gajic, Dragoljub
Gligorijevic J.
Savic Gajic, Ivana
Georgieva O.
Di Gennaro, Stefano
Issue Date: 2017
Abstract: © 2016 Elsevier Ltd Early fault detection and diagnosis plays an increasingly important role in various energy systems where it is critical to avoid deteriorating condition, degraded efficiency and unexpected failures. Rolling-element bearings are among the most common components of rotating machinery used for transformation of energy. Mechanical wear and defective bearings cause rotating machinery to decrease its efficiency, and thus increase energy consumption. A new technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis is presented. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy and the logarithmic energy entropy as a measure of the degree of disorder are extracted in sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is performed by quadratic classifiers. Accuracy of the new technique was tested on four classes of the recorded vibrations signals, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique will be further tested and implemented in a real production environment.
Type: article
DOI: 10.1016/
ISSN: 0360-5442
SCOPUS: 2-s2.0-84996563875
Appears in Collections:Faculty of Engineering, Kragujevac

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