Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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. Preprints 2020, 2020050386 (doi: 10.20944/preprints202005.0386.v1). Tarek, B. A New Health Assessment Prediction Approach: Multi-Scale Ensemble Extreme Learning Machine. Preprints 2020, 2020050386 (doi: 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.

Subject Areas

remaining useful life; c-mapss; extreme learning machine; prognostic and health management; neural networks

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.