Rolling bearing performance degradation evaluation using grey entropy

Abstract

Entropy-based nonlinear dynamic techniques have been considered to be a powerful tool and widely and successfully utilized to analyze the vibration signals in rolling bearing. This paper presents a new entropy-based nonlinear dynamic method, called the Grey entropy (GreyEn) for vibration performance degradation evaluation of rolling bearing. Similar to the existing measures, GreyEn is the negative natural logarithm of the conditional probability that two vectors similar for m points remain similar for the next m+1 points. Importing the concept of grey system theory, vectors’ similarity is measured by the grey relational degree (GRD). Simulation signals are employed to evaluate the effectiveness of the proposed method. The results suggest that GreyEn, compared with sample entropy (SampEn) and fuzzy entropy (FuzzyEn), leads to more efficiently measure the complexity. The validity of GreyEn is also assessed through three experimental tests. The experimental results show that GreyEn, compared with SampEn and FuzzyEn, can effectively evaluate the vibration performance degradation of the rolling bearing.