Detection of bearing damage by statistic vibration analysis (diagnosis using the excess, the concept of crest factor)

The condition of bearings, which are essential components in mechanisms, is crucial to safety. The analysis of the bearing vibration signal, which is always contaminated by certain types of noise, is very important standard for mechanical condition diagnosis of the bearing and mechanical failure phenomenon. In this paper the method of rolling bearing fault detection by statistical analysis of vibration is proposed to filter out Gaussian noise contained in raw vibration signal. The results of experiments show that the vibration signal can be significantly enhanced by using the proposed method. Besides, the proposed method is used to analyze real acoustic signals of bearing with inner race and outer race faults, respectively. Attribute values are agreed with the degree of fault. The results confirm that the periods between the transients, which represent bearing fault characteristics, can be detected successfully.