Next Article in Journal
A Room Temperature Nitric Oxide Gas Sensor Based on a Copper-Ion-Doped Polyaniline/Tungsten Oxide Nanocomposite
Previous Article in Journal
A Robust Trust Establishment Scheme for Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(3), 7062-7083; doi:10.3390/s150307062

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

1
Department of Construction and Manufacturing Engineering, University of Oviedo, Gijón 33204, Spain
2
Department of Mathematics, University of Oviedo, Oviedo 33007, Spain
3
Department of Mining Engineering and Exploitation, University of Oviedo, Oviedo 33004, Spain
4
Department of Electrical Engineering, University of Oviedo, Gijón 33204, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 18 January 2015 / Revised: 24 February 2015 / Accepted: 6 March 2015 / Published: 23 March 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1934 KB, uploaded 23 March 2015]   |  

Abstract

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. View Full-Text
Keywords: prognostics; aircraft engine; remaining useful life; principal component analysis (PCA); dendrogram; classification and regression trees (CART); multivariate adaptive regression splines (MARS) prognostics; aircraft engine; remaining useful life; principal component analysis (PCA); dendrogram; classification and regression trees (CART); multivariate adaptive regression splines (MARS)
Figures

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lasheras, F.S.; Nieto, P.J.G.; de Cos Juez, F.J.; Bayón, R.M.; Suárez, V.M.G. A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines. Sensors 2015, 15, 7062-7083.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top