Signal Spectrum Based Condition Monitoring of Electrical Machines Based on Low Sampling Rate
Date
2024-06
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E
Abstract
In the pursuit of enhancing condition monitoring techniques for electrical machines,
this research addresses the intricate challenge of sampling rates in data acquisition.
Conventional high-frequency sampling, while providing rich data, incurs substantial
costs in memory, processing power, and computational time. Additionally, it introduces
complexities related to data loss during acquisition setup. Aiming for a more balanced
approach, this study explores the potential of low sampling rates, acknowledging the
trade-offs in signal resolution. An innovative algorithm is proposed to harness the
advantages of low sampling rates, circumventing the pitfalls of spectral leakages that
often plague signal analysis. Notably, the proposed methodology eliminates the need
for windowing functions, a traditional requirement in spectral analysis. The intricate
process of window selection, crucial for narrowing the main lobe and reducing leakage
energy, necessitates specialized knowledge. The proposed algorithm simplifies this
aspect, presenting an effective solution without compromising analytical precision.
This study investigates the feasibility and effectiveness of using a low sampling rate of
2 kHz for the condition monitoring of electrical machines, specifically targeting the
detection of broken rotor bars (BRBs).
Description
In the pursuit of enhancing condition monitoring techniques for electrical machines,
this research addresses the intricate challenge of sampling rates in data acquisition.
Conventional high-frequency sampling, while providing rich data, incurs substantial
costs in memory, processing power, and computational time.
Keywords
Induction Motor, Fault Diagnosis, Condition Monitoring, Fourier Transform