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

Zhongzhe Chen, Shuchen Cao and Zijian Mao
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Zhongzhe Chen: School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China
Shuchen Cao: Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Zijian Mao: School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China

Energies, 2017, vol. 11, issue 1, 1-14

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.

Keywords: prognostics; residual useful life; similarity-based approach; supporting vector machine (SVM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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