The performance degradation of a dried vacuum pump utilizing statistical time domain parameters and STFT vibration signal monitoring.
Intelligent data-driven health performance of manufacturing tools is the key element in reducing costs and establishing the right maintenance schedule. With the correct instruments to monitor the reduction in performance of manufacturing tools, companies can plan maintenance work systematically. Thi...
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| Main Authors: | , , , |
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| 格式: | Conference or Workshop Item |
| 出版: |
2023
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/107311/ http://dx.doi.org/10.1063/5.0154235 |
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| 機構: | Universiti Teknologi Malaysia |
| 總結: | Intelligent data-driven health performance of manufacturing tools is the key element in reducing costs and establishing the right maintenance schedule. With the correct instruments to monitor the reduction in performance of manufacturing tools, companies can plan maintenance work systematically. This paper examines measured vibration signals for the health monitoring of dried vacuum pumps. In semiconductor industries, monitoring of the performance of the dried vacuum pump is very critical, not only for early maintenance work before catastrophic failures, but also for systematic pumping switch operations. Statistical time domain and Short-time Fourier transform (STFT) parameters are used for the identification of the vacuum pump condition in this paper. |
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