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A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine

This version is not peer-reviewed.

Submitted:

22 May 2020

Posted:

24 May 2020

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Abstract
This work can be considered as a first step of designing a future competitive data-driven approach for remaining useful life prediction of aircraft engines. The proposed approach is an ensemble of serially connected extreme learning machines. The results of prediction of the first networks are scaled and fed to the next networks as an additive features to the original inputs. This feature mapping allows increasing the correlation of training inputs with their targets by holding new prior knowledge about the probable behavior of the target function. The proposed approach is evaluated under remaining useful estimation using a set of “time-varying” data retrieved from the public dataset C-MAPSS (Commercial Modular Aero Propulsion System Simulation) provided by NASA. The prediction performances are compared to basic extreme learning machine and proved the effectiveness of the proposed methodology.
Keywords: 
remaining useful life; c-mapss; extreme learning machine; prognostic and health management; neural networks
Subject: 
Public Health and Healthcare  -   Public Health and Health Services
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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