Name
Abstract | ||
|---|---|---|
In the motor current signal, the characteristic frequency of broken rotor bar (BRB) fault is modulated by the supply frequency and it decreases with the decrease of the load, resulting it to be easily buried under light load conditions. Teager-Kaiser energy operator (TKEO) has shown better performance to detect the BRB faults than classical methods, such as envelope analysis and spectral analysis. However, the original definition of TKEO leads to its result lack of physical meanings and the causal processing in TKEO can lead to phase distortion and nonideal filter characteristics. Therefore, this article proposes a normalized frequency-domain energy operator (FDEO) for the BRB fault diagnosis, which does not require causal processing and calculates multiple differentiations in the frequency domain with equal accuracy in one operation. Furthermore, the normalized FDEO removes the influence of the supply frequency followed by the spectral analysis to extract fault features. The mathematical model of induction motor (IM) under healthy and faulty condition is studied in this article. Then, the proposed approach is experimentally validated with seeded one and two BRB faults operating under various load conditions. To verify the effectiveness, the results are compared with the TKEO, envelope analysis, and spectral analysis. It was found that the proposed method provides slightly obvious fault features with respect to the TKEO, especially when the IMs run under light load conditions with two BRB faults. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIM.2020.3009011 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Broken rotor bar (BRB), frequency domain energy operator (FDEO), fault diagnosis, induction motor (IM), motor current signature analysis (MCSA) | Journal | 70 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyang Li | 1 | 0 | 0.34 |
| Guojin Feng | 2 | 0 | 0.34 |
| Dong Zhen | 3 | 6 | 2.20 |
| Fengshou Gu | 4 | 23 | 23.43 |
| Andrew Ball | 5 | 72 | 17.29 |