Abstract
Online vibration monitoring is a widely recognised approach to maximise availability of rotating machines such as induction motor-driven centrifugal pumps. As vibration monitoring relies on the analysis of a well-defined multitude of harmonics of the fundamental frequency (i.e. the motor speed), accurate speed estimation is a prerequisite for continuous vibration monitoring systems without tachometers. While numerous methods have been reported for this estimation, their accuracy for well-balanced induction motor drives running at constant speed is insufficient to accurately establish high-frequency harmonics in the spectrum. This paper presents a novel method that automatically and accurately determines the rotational speed from an individual vibration measurement at constant speed using minimal a priori knowledge of the measured system. The proposed method determines the rotational speed with a root mean square error of 0.10 Hz or a specified maximum error of 0.0165 Hz for 90.1% of cases in the recent NLN-EMP dataset. This dataset contains over 2300 measurements, with a significant variety of healthy and faulty electric motor drive vibration signals, including well-balanced and well-aligned cases, which demonstrates the wide applicability of the proposed method. Finally, a benchmark comparison shows that the proposed method significantly improves upon a comparable method from the literature.
| Original language | English |
|---|---|
| Article number | 106475 |
| Number of pages | 11 |
| Journal | Results in Engineering |
| Volume | 27 |
| DOIs | |
| Publication status | Published - Sept 2025 |
Keywords
- UT-Gold-D
- Condition based maintenance
- Condition monitoring
- Rotational speed estimation
- Vibration analysis
- Automated analysis
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NLN-EMP - Motor Current and Vibration Monitoring Dataset for various Faults in an E-motor-driven Centrifugal Pump
Bruinsma, S. (Creator), Geertsma, R. (Creator), Loendersloot, R. (Creator) & Tinga, T. (Creator), 4TU.Centre for Research Data, 5 Aug 2024
DOI: 10.4121/2b61183e-c14f-4131-829b-cc4822c369d0.v4, https://data.4tu.nl/datasets/2b61183e-c14f-4131-829b-cc4822c369d0/4
Dataset
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