A scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy

Abstract

The Shannon entropy measure, applied to the time frequency distribution of a signal, is a reliable indicator to quantitatively analyze the health status of a rolling element bearing. Usually, however, conventional time frequency representations rely on the selection of the best scale of a base function in order to deal with the noise components present in a vibration signal. In this context, the time frequency manifold is a relatively new time frequency analysis method, capable of cancelling out or suppressing the majority of noise components by correlating the deterministic information of a faulty signal in its multidimensional phase space representation, rather than depending on the scale selection of a certain base function. With the aim of examining the merits of the time frequency manifold, in this research, the concepts of the time frequency manifold and Shannon entropy are integrated, so as to construct an accurate bearing health monitoring index. The effects of embedding dimension and time delay on the calculation of the proposed health index are studied. Simulated signals are employed in order to study the characteristic properties of the proposed index. Two experimental datasets are used to validate the effectiveness of the proposed method in compare to some conventional and hybrid bearing health monitoring indexes. Our research shows that the proposed index demonstrates a consistently good performance for both inner and outer race failure, in cases of run to failure bearing testing.