Fault Diagnosis of Rolling Bearing Based on Feature Extraction and Neural Network Algorithm
نویسندگان
چکیده
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 corrupted by noise from other sources. Hence, the fault-related vibration must be recovered from among those sources for accurate diagnosis. In this paper, envelope analysis and FFT analysis used for feature extraction. Back propagation neural network algorithm used for fault diagnosis on rolling bearing. Key-Words: Rolling bearing, fault diagnosis, back-propagation artificial neural network algorithm, envelope detector, Fast Fourier Transform
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