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Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN
http://hdl.handle.net/10295/00006161
http://hdl.handle.net/10295/000061611cc04256-c094-40fb-8158-c87b79dd7363
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
kokuA_2022_7.pdf (4.0 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2023-02-18 | |||||
| タイトル | ||||||
| タイトル | Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 主題 | ||||||
| 主題Scheme | Other | |||||
| 主題 | bearing diagnosis | |||||
| 主題 | ||||||
| 主題Scheme | Other | |||||
| 主題 | electric motor | |||||
| 主題 | ||||||
| 主題Scheme | Other | |||||
| 主題 | vibration analysis | |||||
| 主題 | ||||||
| 主題Scheme | Other | |||||
| 主題 | signal processing | |||||
| 主題 | ||||||
| 主題Scheme | Other | |||||
| 主題 | 1-D CNN | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 作成者 |
Yandagsuren, Dorjsuren
× Yandagsuren, Dorjsuren× Kurauchi, Tatsuki× Toriya, Hisatoshi× Ikeda, Hajime× Adachi, Tsuyoshi× Kawamura, Youhei |
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| 内容記述 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | 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. | |||||
| 言語 | en | |||||
| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
| 書誌情報 |
en : Journal of Sustainable Mining 巻 22, 号 1, p. 65-80, 発行日 2023 |
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| 収録物識別子 | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 25434950 | |||||
| 出版者 | ||||||
| 出版者 | Głowny Instytut Gornictwa (Central Mining Institute) | |||||
| 関連情報 | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.46873/2300-3960.1377 | |||||
| 権利情報 | ||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. | |||||
