Hjorth Parameters for Broken Rotor Bars Failures Characterization in Induction Motors

Fault detection and online condition monitoring of induction motors (IMs) play a critical role in the industry. Regarding this matter, scientists worldwide have developed different methods to monitor and detect various types of damages in IMs, such as damaged bearings, misalignment, and broken rotor...

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Vydáno v:IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (Online) Ročník 7; s. 1 - 5
Hlavní autoři: Barcenas-Peralta, R. D., Perez-Ramirez, C. A., Rivera-Guillen, J. R., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., Hernandez-Maldonado, R., de Santiago-Perez, J. J.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.10.2023
Témata:
Bars
Broken rotor bars
Clustering methods
Hjorth parameters
Induction motor
Induction motors
Industries
K-means clustering method
Monitoring
Proposals
Rotors
ISSN:2573-0770
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Shrnutí:Fault detection and online condition monitoring of induction motors (IMs) play a critical role in the industry. Regarding this matter, scientists worldwide have developed different methods to monitor and detect various types of damages in IMs, such as damaged bearings, misalignment, and broken rotor bars (BRBs), among others. In specific, the detection of a BRB has been the subject of special interest as it poses challenges when attempting early detection and can quickly evolve into catastrophic damages if not detected in a timely manner. As a contribution to this issue, this work explores the potential of the Hjorth parameters as indicators of BRBs using the current signals of an IM under different operating conditions, i.e., healthy (HLT), half BRB, one BRB, and two BRBs. The results obtained indicate that the Hjorth parameters are sensitive to the previously mentioned conditions, allowing the proposal of pattern recognition schemes for automatic classification. For this task, the k-means clustering method is proposed in this work because of its easy implementation. The obtained results confirm that the suggested techniques are reliable to monitor the IMs condition, reaching an accuracy of 98.75%.
ISSN:2573-0770
DOI:10.1109/ROPEC58757.2023.10409416