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Engine Health Assessment and Prediction Using the Group Method of Data Handling and the Method of Match Matrix: Autoregressive Moving Average

Abstract : This research aims to develop a methodology for engine health assessment and long-term anomaly prediction of the engine. This paper presents methodology for engine health assessment and prediction, which includes the following two steps: (1) engine health assessment and anomaly detection is conducted based on an inductive learning technique called Group Method of Data Handling (GMDH), and (2) engine health prediction is conducted based on the Match Matrix – Autoregressive Moving Average (ARMA) model method. Results from an industry case study illustrate the presented methodology's effectiveness in engine health assessment and prediction. An effective anomaly prediction process capable of providing an early warning of a fault in an engine would be of tremendous value to the user and could result in substantial financial savings.
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https://hal.univ-lorraine.fr/hal-03091072
Contributor : Kondo Adjallah Connect in order to contact the contributor
Submitted on : Wednesday, December 30, 2020 - 3:24:42 PM
Last modification on : Tuesday, October 26, 2021 - 8:14:03 AM

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Haixia Wang, Jay Lee, Takahiro Ueda, Kondo Hloindo Adjallah, Masoud Ghaffari. Engine Health Assessment and Prediction Using the Group Method of Data Handling and the Method of Match Matrix: Autoregressive Moving Average. ASME Turbo Expo 2007: Power for Land, Sea, and Air, May 2007, Montreal, Quebec, Canada. pp.697-702, ⟨10.1115/GT2007-27672⟩. ⟨hal-03091072⟩

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