Intelligent prognostics based on empirical mode decomposition and extreme learning machine

Abstract

Prognostics and Health Management (PHM) for condition monitoring systems have been proposed for predicting faults and estimating the remaining useful life (RUL) of components or subsystem. For gaining importance in industry and decrease possible loss of production due to machine stopping, a new intelligent method for the tool wear condition monitoring based on features extraction by using Empirical Modes Decomposition (EMD) and nonlinear regression by using improved extreme learning machine (IELM). Features extraction from raw sensor data is the essential step for the construction of an effective PHM. The IELM is a technique where the goodness of fit is measured; The idea is based on the computation of a nonlinear regression function in a high dimensional feature space where the input data mapped via a nonlinear function. The results of its application in CNC machining show that this indicator can reflect effectively the performance degradation of cutting tool's for milling process. The proposed method is applied on real world RUL estimation and health assessment for a given wear limit based on extracted features.