Intelligent Fault Classification of Rolling Bearing at Variable Speed Based on Reconstructed Phase Space

Rolling bearing is a common mechanical part which is subject to be damaged. It is important to monitor the condition of bearing. An effective mean is to extract faulty features of bearing from the vibration signal. In this paper, a method is introduced to realize intelligent classification of bearing state. The vibration signal is reconstructed into phase space by estimating the time delay and embedded dimension of time series. After reconstruction, fault classification is accomplished through normalized principal component analysis. It is testified that this method is effective for classifying fault of bearing by experiment and data analysis