Abstract

Multiscale Permutation Entropy (MPE) has been applied as a non-linear measure for estimating the complexity of
time series. Nevertheless, the coarse-grained procedure in MPE only takes low-frequency information into account. To overcome this shortcoming, in this paper, a new entropy measure, named Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), is proposed to provide stable and reliable results by offering both low-frequency and high-frequency information. Firstly, the F2C signals are created based on the reconstruction of selected wavelet coefficients using wavelet packet decomposition. Then, permutation entropy is used to estimate the complexity and dynamic change of the F2C signals. Experimental analysis is carried out to investigate and compare the performance of the
proposed F2CMPE with that of the MPE. Results indicate that the proposed method can give consistent and stable entropy measure for rolling bearing fault diagnosis.