Randomness complexity as a family feature of rolling bearings’ degradation

Randomness complexity is a kind of features which is widely used to describe bearings’ degradation. However, different randomness complexities present different properties. It is necessary to figure out different randomness complexities’ properties. In this paper, we are going to make comparisons of seven commonly used randomness complexities namely approximate entropy, sample entropy, fuzzy entropy, Shannon entropy, permutation entropy, Lempel-Ziv complexity and C0 complexity by simulation signals with three different aspects and two run-to-failure bearing’s data. By comparisons, we have found that there are a kind of similarity between them and we have proposed a trend similarity index to expound this similarity. Based on the comparisons, we can infer that randomness complexities are a family feature of rolling bearings’ degradation. Among the seven discussed complexities, sample entropy has the best performance, and it can be a good representative of the complexity features. In this paper, the difference between complexity features and other features when monitoring bearings’ degradation have been discussed. The research will provide a reference for rolling bearings’ multi-features dimensionality reduction by attribute selection method