Health index synthetization and remaining useful life estimation for turbofan engines based on run-to-failure datasets

Shi, J.; Li, Y.; Wang, G.; Li, X.

doi:10.17531/ein.2016.4.18

Artykuł - szczegóły

Tytuł artykułu

Health index synthetization and remaining useful life estimation for turbofan engines based on run-to-failure datasets

Autorzy

Shi J. , Li Y. , Wang G. , Li X.

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.17531/ein.2016.4.18

Warianty tytułu

Synteza wskaźników stanu technicznego oraz ocena pozostałego okresu użytkowania silników turbowentylatorowych z wykorzystaniem zbiorów danych o pracy do czasu uszkodzenia

Języki publikacji

Abstrakty

Turbofan engines will gradually degrade until failure occurs or life ends if without maintenance. Reliable degradation assessment and remaining useful life (RUL) estimation make sense on both aviation safety and rational maintenance decisions. This paper proposes a data-driven prognostic method on the premise of run-to-failure (RtF) data which are multivariate sensory data collected from the engines operating from normal to failure. After necessary pre-processing to the data, clustering analysis is executed to generate the clusters which represent the multi-states of the degradation process. The failure state cluster is extracted, and then the distance between the pre-processed data and the cluster is calculated. Therefore, one-dimensional time series are generated and defined as the health indices. Afterwards the degradation models are built based on the health indices. Finally, the RUL of a testing unit can be estimated by similarity analysis with the models. Hierarchical clustering (HC) and relevance vector machine (RVM) are the main algorithms employed in this paper. To validate the proposition, a case study is performed on turbofan engines data from Prognostics Center of Excellence (PCoE) at NASA Ames Research Center, and sufficient comparisons were given.

Silniki turbowentylatorowe niepoddane konserwacji ulegają stopniowej degradacji aż do czasu wystąpienia uszkodzenia lub zakończenia cyklu życia. Rzetelna ocena degradacji oraz pozostałego okresu użytkowania (RUL) mają wpływ zarówno na bezpieczeństwo maszyn lotniczych jak i racjonalne podejmowanie decyzji dotyczących utrzymania ruchu. W artykule zaproponowano sterowaną danymi metodę prognostyczną opartą na danych o pracy do czasu uszkodzenia (run-to failure, RTF), które są wielowymiarowymi danymi sensorycznymi zbieranymi podczas normalnej pracy silnika aż do jego uszkodzenia. Po niezbędnej wstępnej obróbce danych, przeprowadzono analizę skupień w celu wygenerowania skupień reprezentujących multi-stany procesu degradacji. Wyodrębniono klaster stanów uszkodzenia, a następnie obliczono odległość między wstępnie przetworzonymi danymi a wyodrębnionym klastrem. Następnie wygenerowano jednowymiarowe szeregi czasowe, które zdefiniowano jako wskaźniki stanu technicznego. Na podstawie tych wskaźników zbudowano modele degradacji. Wreszcie, w oparciu o analizę podobieństwa do opracowanych modeli oceniono RUL jednostki testowej. Główne algorytmy zastosowane w niniejszym opracowaniu to algorytmy grupowania hierarchicznego (HC) oraz maszyny wektorów istotnych (RVM). Aby zweryfikować zaproponowaną w pracy metodę, przeprowadzono studium przypadku z wykorzystaniem danych dot. silników turbowentylatorowych pochodzące z Prognostic Center of Excellence (PCoE) przy NASA Ames Research Center oraz przedstawiono odpowiednie porównania.

Słowa kluczowe

hierarchical clustering relevance vector machine run-to-failure remaining useful life health indices prognostics

grupowanie hierarchiczne maszyna wektorów istotnych praca do czasu uszkodzenia pozostały okres użytkowania wskaźnik stanu technicznego prognostyka

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2016

Tom

Vol. 18, no. 4

Strony

621--631

Opis fizyczny

Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Shi J.

sjm@csu.ac.cn

Technology and Engineering Center for Space Utilization Chinese Academy of Sciences No.9 South Dengzhuang Rd, Haidian Dist Beijing, 10094, China

autor

Li Y.

liyongxiang@csu.ac.cn

Technology and Engineering Center for Space Utilization Chinese Academy of Sciences No.9 South Dengzhuang Rd, Haidian Dist Beijing, 10094, China

autor

Wang G.

wanggong@csu.ac.cn

Technology and Engineering Center for Space Utilization Chinese Academy of Sciences No.9 South Dengzhuang Rd, Haidian Dist Beijing, 10094, China

autor

Li X.

xzhli@csu.ac.cn

Technology and Engineering Center for Space Utilization Chinese Academy of Sciences No.9 South Dengzhuang Rd, Haidian Dist Beijing, 10094, China

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