Multiscale Diversity Index for RUL Analysis with Bernstein Polynomial Neural Networks

This paper employs multiscale feature extraction based on Simpson's diversity index for predicting remaining useful life (RUL) of bearings. Being a measure of variety of elements in the given time series, Simpson's diversity index (SDI) acts as a feature which is assumed to be different for time series of different quality. Thus, RUL is considered to be function of multiscale SDI in this paper. Features are mapped to RUL with modified Tensor product Bernstein polynomial (TPBP) network. The aim of this paper is to test SDI based feature extraction together with modified TPBP network for in the context of RUL analysis.