A hybrid approach for fault diagnosis of spur gears using Hu invariant moments and artificial neural networks

• Autorzy: Rex F. Michael Thomas , Andrews A. , Krishnakumari A. , Hariharasakthisudhan P.

Identyfikatory

DOI

10.24425/mms.2020.134587

• Języki publikacji: EN

Abstrakty

EN

Achieving a reliable fault diagnosis for gears under variable operating conditions is a pressing need of industries to ensure productivity by averting unwanted breakdowns. In the present work, a hybrid approach is proposed by integrating Hu invariant moments and an artificial neural network for explicit extraction and classification of gear faults using time-frequency transforms. The Zhao-Atlas-Marks transform is used to convert the raw vibrations signals from the gears into time-frequency distributions. The proposed method is applied to a single-stage spur gearbox with faults created using electric discharge machining in laboratory conditions. The results show the effectiveness of the proposed methodology in classifying the faults in gears with high accuracy.

Słowa kluczowe

EN

gear fault; Ahao-Atlas-Marks; time-frequency domain features; Hu invariant moments; ANN

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2020
• Tom: Vol. 27, nr 3
• Strony: 451--464
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr., wzory

Twórcy

autor

Rex F. Michael Thomas

• National Engineering College, Department of Mechanical Engineering, Kovilpatti - 628 503, Tamil Nadu, India

autor

Andrews A.

• National Engineering College, Department of Mechanical Engineering, Kovilpatti - 628 503, Tamil Nadu, India

autor

Krishnakumari A.

• Hindustan Institute of Technology and Science, Department of Mechanical Engineering, Chennai – 603103, Tamil Nadu, India

autor

Hariharasakthisudhan P.

• National Engineering College, Department of Mechanical Engineering, Kovilpatti - 628 503, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).