A novelty detection approach to monitoring of epicyclic gearbox health

• Autorzy: Dworakowski Z. , Dziedziech K. , Jabłoński A.

Identyfikatory

DOI

10.24425/123896

• Języki publikacji: EN

Abstrakty

EN

Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries in which these gearboxes operate in a harsh environment and in variable operational conditions. In this paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health state based on vectors of features calculated from the vibration signal. The authors examine various feature vectors, various sources of data and many different damage scenarios in order to compare novel detection algorithms based on three different principles of operation: a distance in the feature space, a probability distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate for non-deterministic results of training of neural networks, which may lead to different network performance, the ensemble technique is used to combine responses from several networks. The methods are tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes, and acquisition of data at variable speed conditions.

Słowa kluczowe

EN

epicyclic gearbox; soft computing; auto-associative neural network; novelty detection; vibration signal

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 3
• Strony: 459--473
• Opis fizyczny: Bibliogr. 32 poz., fot., rys., tab., wykr.

Twórcy

autor

Dworakowski Z.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059, Cracow, Poland

autor

Dziedziech K.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059, Cracow, Poland

autor

Jabłoński A.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059, Cracow, Poland

Bibliografia

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Uwagi

EN

1. The work presented in this paper was supported by the National Centre for Research and Development in Poland under the research project no. PBS3/B6/21/2015.

PL

2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).