A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data PROJECT TITLE :A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic DataABSTRACT:In this paper, we tend to propose to outline the problem of predicting the remaining helpful life of a component as a binary classification task. This approach is particularly useful for problems in that the evolution of the system condition is described by a mixture of a big range of discrete-event diagnostic data, and for which various approaches are either not applicable, or are solely applicable with vital limitations or with a massive computational burden. The proposed approach is demonstrated with a case study of real discrete-event information for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen as a result of of its sensible generalization ability, computational potency, and low needs on parameter tuning. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Finite-Time Cluster Synchronization of T–S Fuzzy Complex Networks With Discontinuous Subsystems and Random Coupling Delays Impedance-Differential Protection: A New Approach to Transmission-Line Pilot Protection