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

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.

• Publication year

  2017

• Venue

  Energies

• Publication date

  2017-12-23

• Fields of study

  Engineering

• Identifiers
  DOI 10.3390/EN11010028
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• Evaluation on the 2008 PHM data challenge competition data supports the method's effectiveness and practicality.

  Confidence 0.90

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• A new state of health indicator is introduced through preprocessing of the raw engine data.

  Confidence 0.92

  박진우 (dztg5apj7m) extraction뀨 (7c402c1b98) review--------- ✂ Cut Here ✂ --------- (jqthcshryb) reviewAK (4715169a40) reviewB (s683577b42) reviewGahyoun (29ua5897xm) review
• A remaining useful life estimation framework is built from whole-life-cycle data and performance-deterioration parameters from engines without failures, using similarity theory and a support vector machine model.

  Confidence 0.97

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• 2008 phm data challenge competition data

  dataset, benchmark

  The benchmark aero-engine dataset from the 2008 PHM data challenge used for validation.

  Aliases: PHM 2008 data challenge data, 2008 PHM dataset

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• performance-deteriorated parameter data

  dataset, feature set

  Measured engine performance parameters that show degradation over time and are used as prognostic inputs.

  Aliases: deteriorated parameter data

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• raw data preprocessing

  method, processing step

  The data-cleaning and transformation step applied before constructing the health indicator and model inputs.

  Aliases: preprocessing of raw data

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• remaining useful life estimation

  task, estimation problem

  The task of predicting how much operating life remains before an aircraft engine reaches end of life or failure.

  Aliases: RUL estimation, RUL

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• similarity theory

  method, theory

  A similarity-based modeling approach used to relate engine degradation patterns for prognostic estimation.

  Aliases: similarity approach

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• state of health indicator

  indicator, feature

  A derived indicator summarizing the health condition of the engine from monitored data.

  Aliases: SOH indicator, SOH

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• support vector machine

  machine learning model, method

  A supervised learning model used here to map engine features to remaining-life estimates.

  Aliases: SVM

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• whole lifecycle data

  dataset, data source

  Engine monitoring data collected across the full operating life of the engine.

  Aliases: life-cycle data, full lifecycle data

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