Bearing Fault Feature Extraction by Recurrence Quantification Analysis
نویسندگان
چکیده
In rotating machinery one of the critical components that is prone to premature failure is the rolling bearing. Consequently, early warning of an imminent bearing failure is much critical to the safety and reliability of any high speed rotating machines. This study is concerned with the application of Recurrence Quantification Analysis (RQA) in fault detection of rolling element bearings in rotating machinery. Based on the results from this study it is reported that the RQA variable, percent determinism, is sensitive to the type of fault investigated and therefore can provide useful information on bearing damage in rolling element bearings. Keywords—Bearing fault detection, machine vibrations, nonlinear time series analysis, recurrence quantification analysis.
منابع مشابه
A Method of Bearing Fault Feature Extraction Based on Improved Wavelet Packet and Hilbert Analysis
In order to supply a gap of current resonance vibration and STFT demodulation method applied to rolling bearing fault feature extraction of city rail vehicle, a fault diagnosis method for rolling bearing is presented, which is based on the integration of improved wavelet packet, frequency energy analysis and Hilbert marginal spectrum. When faults occur in rolling bearing, the energy of the roll...
متن کاملWire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network
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....
متن کاملSpectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals
Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to hig...
متن کاملFault Diagnosis Method Based on Kurtosis Wave and Information Divergence for Rolling Element Bearings
Fault diagnosis depends largely on feature analysis of vibration signals. However, feature extraction for fault diagnosis is difficult because the vibration signals often contain a strong noise component. Noises stronger than the actual fault signal may interfere with diagnosis and ultimately cause misdiagnosis. In order to extract the feature from a fault signal highly contaminated by the nois...
متن کاملFault features extraction for bearing prognostics
This paper describes a newly developed fault feature extraction method for bearing prognostics. The effectiveness of the method is demonstrated with real bearing run-to-failure test data. Experimental results show that with the growth of the bearing defective area, the method is able to indicate clearer trends than the traditional condition indicators, such as RMS, the peak value, the amplitude...
متن کامل