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Plastic gear remaining useful life prediction using artificial neural network

Vorhersage der Restnutzungsdauer von Kunststoffzahnrädern mit künstlichem neuronalem Netz

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Abstract

Prognostic and health management (PHM) of plastic gears has attracted attention due to an increasing performance of plastic gears, uncovering potential applications in the industry, especially in vehicle transmissions. Meanwhile, health indicator (HI) construction and remaining useful life (RUL) estimation are two key elements to efficiently perform PHM. In this paper, a health indicator generator (HIG) based on an artificial neural network (ANN) is constructed. The HIG is learned from training data extracted from plastic gears’ raw vibration data using the Fourier decomposition method (FDM) in a sensitive frequency band (SFB) and labeled using a change-point detection algorithm (CDA). Three prediction strategies, including linear regression (LR), estimation of parameters for Weibull distribution (EWD), HI combined average RUL (HI-ARUL), are deployed using HI generated from HIG to predict the RUL of the plastic gear. The results show that the generated HI is sensitive to the early failure of plastic gears and is capable of applying an efficient and precise diagnosis method, which can be performed during the whole working time of plastic gear with prediction errors smaller than 7%.

Zusammenfassung

Das Prognose- und Gesundheitsmanagement (PHM) von Kunststoffzahnrädern hat durch die zunehmende Leistungsfähigkeit von Kunststoffzahnrädern und deren Einsatzmöglichkeiten in der Industrie, insbesondere in Fahrzeuggetrieben, Aufmerksamkeit erregt. In der Zwischenzeit sind die Konstruktion des Zustandsindikators (HI) und die Schätzung der Restnutzungsdauer (RUL) zwei Schlüsselelemente für eine effiziente Durchführung von PHM. In diesem Artikel wird ein Gesundheitsindikatorgenerator (HIG) basierend auf einem künstlichen neuronalen Netz (ANN) konstruiert. Das HIG wird aus Trainingsdaten gelernt, die aus den Rohschwingungsdaten von Kunststoffzahnrädern mit der Fourier-Zerlegungsmethode (FDM) in einem empfindlichen Frequenzband (SFB) extrahiert und mit einem Änderungspunkt-Erkennungsalgorithmus (CDA) gekennzeichnet werden. Drei Vorhersagestrategien, einschließlich lineare Regression (LR), Schätzung von Parametern der Weibull-Verteilung (EWD), HI kombinierter Durchschnitt RUL (HI-ARUL), werden unter Verwendung von HI, das aus HIG generiert wird, eingesetzt, um die RUL des Kunststoffzahnrads vorherzusagen. Die Ergebnisse zeigen, dass der erzeugte HI empfindlich auf den frühen Ausfall von Kunststoffzahnrädern reagiert. Daher ist es möglich, ein effizientes und präzises Diagnoseverfahren anzuwenden, das während der gesamten Arbeitszeit von Kunststoffzahnrädern mit Vorhersagefehlern von weniger als 7% durchgeführt werden kann.

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Authors and Affiliations

  1. Kyoto Institute of Technology, Matsugasaki, 606-8585, Kyoto, Japan

    B. H. Kien, D. Iba, Y. Tsutsui, D. Taga, N. Miura, T. Iizuka, A. Masuda & I. Moriwaki

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  1. B. H. Kien
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  2. D. Iba
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  3. Y. Tsutsui
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  4. D. Taga
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Correspondence to B. H. Kien.

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Kien, B.H., Iba, D., Tsutsui, Y. et al. Plastic gear remaining useful life prediction using artificial neural network. Forsch Ingenieurwes 86, 569–585 (2022). https://doi.org/10.1007/s10010-021-00557-9

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