Hjorth Parameters for Broken Rotor Bars Failures Characterization in Induction Motors

• Published in: 2023 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Vol. 7; pp. 1 - 5
• Main Authors: 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.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.10.2023
• Subjects: Bars; Broken rotor bars; Clustering methods; Hjorth parameters; Induction motor; Induction motors; Industries; K-means clustering method; Monitoring; Proposals; Rotors
• ISSN: 2573-0770
• DOI: 10.1109/ROPEC58757.2023.10409416

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%.