Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

• Autorzy: Yandagsuren Dorjsuren , Kurauchi Tatsuki , Toriya Hisatoshi , Ikeda Hajime , Adachi Tsuyoshi , Kawamura Youhei

Identyfikatory

DOI

10.46873/2300-3960.1377

• Języki publikacji: EN

Abstrakty

EN

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.

Słowa kluczowe

EN

bearing diagnosis; electric motor; vibration analysis; signal processing; 1-D CNN

PL

diagnostyka łożysk; silnik elektryczny; analiza drgań; przetwarzanie sygnałów; 1-D CNN

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2023
• Tom: Vol. 22, iss. 1
• Strony: 65--80
• Opis fizyczny: Bibliogr. 51 poz.

Twórcy

autor

Yandagsuren Dorjsuren

• Akita University, Faculty of International Resource Sciences, Japan

• https://orcid.org/0000-0002-5307-931X

autor

Kurauchi Tatsuki

• Akita University, Faculty of International Resource Sciences, Japan

• https://orcid.org/0000-0002-6409-331X

autor

Toriya Hisatoshi

• Akita University, Faculty of International Resource Sciences, Japan

• https://orcid.org/0000-0001-6753-5681

autor

Ikeda Hajime

• Akita University, Faculty of International Resource Sciences, Japan

• https://orcid.org/0000-0002-0863-5583

autor

Adachi Tsuyoshi

• Akita University, Faculty of International Resource Sciences, Japan

• https://orcid.org/0000-0003-0556-7527

autor

Kawamura Youhei

• Hokkaido University, Division of Sustainable Resources Engineering, Japan

• https://orcid.org/0000-0003-4103-762X

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