Predictive maintenance for aircraft engines using data fusion

Yupeng Wei, Dazhong Wu, Janis Terpenny

Research output: Contribution to conferencePaper

Abstract

The airline industry spent over $60 billion on maintenance, repair and overhaul of aircraft engines in 2014. This cost is estimated to reach $90 billion in 2024. Many believe that effective prognostics and health monitoring (PHM) systems for aircraft engines will significantly reduce maintenance costs as well as increase the remaining useful life (RUL) of aircraft engines. While in general, model-based prognostic approaches have been demonstrated for damage propagation prediction, little research has been reported on the effectiveness of data-driven prognostics for aircraft engines. This paper presents a new methodology that estimates the RUL of an aircraft engine using multiple sensors and random forests. This new method is demonstrated on a dataset generated by the commercial modular aero-propulsion system simulation (C-MAPSS). Experimental results have shown that a relative error rate of 0.39% can be achieved.

Original languageEnglish (US)
Pages895-900
Number of pages6
StatePublished - Jan 1 2018
Event2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018 - Orlando, United States
Duration: May 19 2018May 22 2018

Other

Other2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018
CountryUnited States
CityOrlando
Period5/19/185/22/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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