Abstract
This paper presents the broken rotor bar fault diagnostics by time-frequency analysis of motor current under extended startup transient time achieved by reducing the applied voltage. The fault diagnostics under a steady stage regime has been a topic of interest since the past few decades. The main aim has been focused on the detection of fault-based frequencies which are the function of slip. Those frequencies become very less legible under low load conditions and totally disappear under no-load conditions. Moreover, the stator and rotor slot skews have a potential attenuation impact on them. To avoid these problems, the time-frequency analysis of motor startup current is investigated in this paper using a wavelet approach. To improve the legibility of the spectrum, the transient time is extended by reducing the supply voltage of the machine under no external load. By reducing the supply voltage, the inertia of the rotor acts as a load to increase the transient time which is essential for better resolution. The results are based on the practical measurements taken from the laboratory setup under healthy and faulty conditions.
Original language | English |
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Title of host publication | Proceedings of the International Conference on Electrical Machines, ICEM 2020 |
Publisher | IEEE |
Pages | 1481-1487 |
Number of pages | 7 |
ISBN (Electronic) | 9781728199450 |
DOIs | |
Publication status | Published - 23 Aug 2020 |
MoE publication type | A4 Conference publication |
Event | International Conference on Electrical Machines - Virtual, Online Duration: 23 Aug 2020 → 26 Aug 2020 Conference number: 25 |
Publication series
Name | Proceedings (International Conference on Electrical Machines) |
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Publisher | IEEE |
ISSN (Print) | 2381-4802 |
ISSN (Electronic) | 2473-2087 |
Conference
Conference | International Conference on Electrical Machines |
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Abbreviated title | ICEM |
City | Virtual, Online |
Period | 23/08/2020 → 26/08/2020 |
Other | Virtual Conference |
Keywords
- Condition monitoring
- Fault diagnosis
- Fourier transforms
- Induction motors
- Wavelet transforms