A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering

This paper considers a quantitative method for assessment of fault severity of rolling element bearing by means of signal complexity and morphology filtering. The relationship between the complexity and bearing fault severity is explained. The improved morphology filtering is adopted to avoid the ambiguity between severity fault and the pure random noise since both of them will acquire higher complexity value. According to the attenuation signal characteristics of a faulty bearing the artificial immune optimization algorithm with the target of pulse index is used to obtain optimal filtering signal. Furthermore, complexity algorithm is revised to avoid the loss of weak impact signal. After largely removing noise and other unrelated signal components, the complexity value will be mostly affected by the bearing system and therefore may be adopted as a reliable quantitative bearing fault diagnosis method. Application of the proposed approach to the bearing fault signals has demonstrated that the improved morphology filtering and the complexity of signal can be used to adequately evaluate bearing fault severity.