Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection

JVC/Journal of Vibration and Control

Volumn 22, Issue 2, 2016, Pages 396-411

  Ziaja, Aleksandra ^a   Antoniadou, Ifigeneia ^b   Barszcz, Tomasz ^a   Staszewski, Wieslaw J ^a   Worden, Keith ^b  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Fault detection; fractal theory; novelty detection; rolling element bearing; self similarity; wavelet based variance

Indexed keywords

ARTIFICIAL INTELLIGENCE; BALL BEARINGS; BEARINGS (MACHINE PARTS); FRACTALS; LEARNING SYSTEMS; NEURAL NETWORKS; ROLLER BEARINGS; SIGNAL PROCESSING; WAVELET ANALYSIS; WIND TURBINES;

FRACTAL THEORY; NOVELTY DETECTION; ROLLING ELEMENT BEARING; SELF-SIMILARITIES; WAVELET-BASED VARIANCE;

FAULT DETECTION;

EID: 84954286132     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546314532859     Document Type: Article

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