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Abstract
Microelectromechanical systems accelerometers have opened new possibilities for vibration monitoring of a rotating machinery. They enable mounting accelerometers directly to the rotating component of the machine, e.g., shaft. This enables not only the measurement of a lateral vibration but also a torsional vibration of the machine. This increases the vibration data gathered from the machine by one measurement instrument. This article presents an on-shaft wireless universal measurement unit (UMU) with innovative combination of features, such as a high measurement range and easy mounting. The UMU has a two-sensor configuration where two accelerometers are mounted to the opposite sides of a shaft. This enables to utilize a novel signal processing method to separate torsional and lateral vibration from the data. The signal processing method combines and modifies methods presented in the published literature. The results presented in this article demonstrate that the UMU together with the presented signal processing method can measure frequencies of torsional and lateral vibration accurately. However, the amplitude comparison between the UMU and reference sensors cannot be done adequately because they are measuring either different components of the machine or different physical properties.
Original language | English |
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Pages (from-to) | 5857-5868 |
Number of pages | 12 |
Journal | IEEE-ASME Transactions on Mechatronics |
Volume | 27 |
Issue number | 6 |
Early online date | 27 Jul 2022 |
DOIs | |
Publication status | Published - 1 Dec 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Accelerometers
- Vibrations
- Sensors
- Shafts
- Vibration measurement
- Signal processing
- Gravity
- Accelerometer
- condition monitoring
- lateral vibration
- microelectromechanical systems (MEMS)
- torsional vibration
Fingerprint
Dive into the research topics of 'On-Shaft Wireless Vibration Measurement Unit and Signal Processing Method for Torsional and Lateral Vibration'. Together they form a unique fingerprint.Projects
- 2 Finished
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AI-ROT/Viitala: Artificial Intelligence Optimization for Production Lines Deploying Rotating Machinery
Viitala, R. (Principal investigator)
01/01/2021 → 31/12/2023
Project: RCF Academy Project targeted call
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Reboot IoT Factory Phase II
Juhanko, J. (Principal investigator)
01/11/2019 → 31/08/2021
Project: Business Finland: Other research funding