Version 1
: Received: 22 May 2020 / Approved: 24 May 2020 / Online: 24 May 2020 (16:24:08 CEST)
How to cite:
Tarek, B. A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine. Preprints2020, 2020050386. https://doi.org/10.20944/preprints202005.0386.v1
Tarek, B. A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine. Preprints 2020, 2020050386. https://doi.org/10.20944/preprints202005.0386.v1
Tarek, B. A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine. Preprints2020, 2020050386. https://doi.org/10.20944/preprints202005.0386.v1
APA Style
Tarek, B. (2020). A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine. Preprints. https://doi.org/10.20944/preprints202005.0386.v1
Chicago/Turabian Style
Tarek, B. 2020 "A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine" Preprints. https://doi.org/10.20944/preprints202005.0386.v1
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 is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
