Rolling element bearing is one of the most important and common components in rotary machines, whose failures can cause both personal damage and economic loss. This paper focuses on condition monitoring and fault diagnosis of rolling element bearing in order to detect the failure ahead of time and estimate the fault location accurately when failure occurs. Wavelet energy entropy is introduced into the field of mechanical condition monitoring and SOM network is used in fault diagnosis of rolling element bearing. In order to validate the effectiveness of the proposed method, a bearing accelerated life test is performed on the accelerated bearing life tester(ABLT-1A). The results indicate that wavelet energy entropy has better performance and can forecast fault development earlier compared with kurtosis and RMS of the vibration signal, while SOM network, which has a advantage of visualization, can distinguish bearing fault type well.
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