Next Article in Journal
Magnet Shape Optimization of Two-Layer Spoke-Type Axial Flux Interior Permanent Magnet Machines
Next Article in Special Issue
A Non-Probabilistic Solution for Uncertainty and Sensitivity Analysis on Techno-Economic Assessments of Biodiesel Production with Interval Uncertainties
Previous Article in Journal
Statistics to Detect Low-Intensity Anomalies in PV Systems
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Energies 2018, 11(1), 28; doi:10.3390/en11010028

Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

1
School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China
2
Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Received: 25 November 2017 / Revised: 13 December 2017 / Accepted: 18 December 2017 / Published: 23 December 2017
View Full-Text   |   Download PDF [2080 KB, uploaded 29 December 2017]   |  

Abstract

As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine’s different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality. View Full-Text
Keywords: prognostics; residual useful life; similarity-based approach; supporting vector machine (SVM) prognostics; residual useful life; similarity-based approach; supporting vector machine (SVM)
Figures

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Chen, Z.; Cao, S.; Mao, Z. Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach. Energies 2018, 11, 28.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top