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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.14448 |
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| _version_ | 1866909296032219136 |
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| author | Lifsitch, Heitor Rocha, Gabriel Bragança, Hendrio Filho, Cláudio Okimoto, Leandro Amorin, Allan Cardoso, Fábio |
| author_facet | Lifsitch, Heitor Rocha, Gabriel Bragança, Hendrio Filho, Cláudio Okimoto, Leandro Amorin, Allan Cardoso, Fábio |
| contents | To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14448 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vibration Sensor Dataset for Estimating Fan Coil Motor Health Lifsitch, Heitor Rocha, Gabriel Bragança, Hendrio Filho, Cláudio Okimoto, Leandro Amorin, Allan Cardoso, Fábio Signal Processing To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions. |
| title | Vibration Sensor Dataset for Estimating Fan Coil Motor Health |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.14448 |