Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing
نویسندگان
چکیده
منابع مشابه
Fault Diagnosis of Rolling Bearing Based on Feature Extraction and Neural Network Algorithm
The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. In complex machines, the vibration generated by a component is easily affected by the vibration of other components or is corrupt...
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The fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. For solving this problem, a feature extraction method combing the Hilbert-Huang transform with singular value decomposition was proposed in this paper. The method includes three steps. Firstly, instantaneous amplitude matrices were obtained by Hilbert-Huang transform from rolling bearing...
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Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals....
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This paper presents a methodology for an automation of fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components. The system uses the wavelet packet decomposition using ‘rbio5.5’ real mother wavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The decomposition level is determined by the samp...
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ژورنال
عنوان ژورنال: Journal of Vibroengineering
سال: 2016
ISSN: 1392-8716
DOI: 10.21595/jve.2016.17566