NDLI: Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

Content Provider	Springer Nature Link
Author	Li, Min Yang, Jianhong Wang, Xiaojing
Copyright Year	2015
Abstract	The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.
Starting Page	1240
Ending Page	1247
Page Count	8
File Format	PDF
ISSN	10009345
Journal	Chinese Journal of Mechanical Engineering
Volume Number	28
Issue Number	6
e-ISSN	21928258
Language	English
Publisher	Chinese Mechanical Engineering Society
Publisher Date	2015-07-14
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	cyclostationary cyclical spectrum density rolling bearing fault diagnosis Mechanical Engineering Theoretical and Applied Mechanics Manufacturing, Machines, Tools Engineering Thermodynamics, Heat and Mass Transfer Power Electronics, Electrical Machines and Networks Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Mechanical Engineering

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