Fault Diagnosis of Bearings Based on Time-Delayed Correlation and Demodulation as Well as B-Spline Fuzzy Neural Networks

摘要

As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore, great attentions have been attributed to the theories 、 failure diagnosis methods and their applications for rolling bearings. Vibration analysis is also a very important means for condition monitoring and fault diagnosis. This paper aims at the research on the methods of signal processing and pattern recognition. Vibration signals collected were analyzed by using methods of Time-delayed correlation demodulation and effective signal features were extracted. B-spline neurofuzzy networks were established to carry out the recognition of faults of bearings. Experimental results have proved that the developed error diagnostic architecture is reliable and effective.