Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s10462-022-10293-3 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8303 |
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oai:localhost:PNK-83032023-04-25T09:20:11Z Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals Bayu Adhi, Tama Malinda, Vania Seungchul, Lee rotating machinery using vibration signals CC BY Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method. 2023-04-25T09:20:11Z 2023-04-25T09:20:11Z 2022 Book https://link.springer.com/article/10.1007/s10462-022-10293-3 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8303 en application/pdf Springer |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
rotating machinery using vibration signals |
| spellingShingle |
rotating machinery using vibration signals Bayu Adhi, Tama Malinda, Vania Seungchul, Lee Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| description |
CC BY |
| format |
Book |
| author |
Bayu Adhi, Tama Malinda, Vania Seungchul, Lee |
| author_facet |
Bayu Adhi, Tama Malinda, Vania Seungchul, Lee |
| author_sort |
Bayu Adhi, Tama |
| title |
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| title_short |
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| title_full |
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| title_fullStr |
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| title_full_unstemmed |
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| title_sort |
recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals |
| publisher |
Springer |
| publishDate |
2023 |
| url |
https://link.springer.com/article/10.1007/s10462-022-10293-3 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8303 |
| _version_ |
1764268032921698304 |
| score |
8.891145 |