FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology

• Published in: IEEE transactions on instrumentation and measurement Vol. 63; no. 5; pp. 1032 - 1040
• Main Authors: de Jesus Rangel-Magdaleno, Jose, Peregrina-Barreto, Hayde, Ramirez-Cortes, Juan Manuel, Gomez-Gil, Pilar, Morales-Caporal, Roberto
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Bars; Broken bars; Failure; fault diagnosis; field programmable gate array (FPGA); Field programmable gate arrays; Induction motors; Instrumentation; Mathematical morphology; MCSA; Morphology; Motors; Semiconductors; Signature analysis; Statistical analysis; Transforms
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2013.2286931

Broken bars detection on induction motors has been a topic of interest in recent years. Its detection is important due to the fact that the failure is silent and the consequences it produces as power consumption increasing, vibration, introduction of spurious frequencies in the electric line, among others, can be catastrophic. In this paper, the use of motor current signature analysis and mathematical morphology to detect broken bars on induction motors under different mechanical load condition is analyzed. The proposed algorithm first identifies the motor load and then the motor condition. The statistical analysis of several tests under different motor loads (100%, 75%, 50%, and 25%) and motor condition (healthy, one broken bar, and two broken bars) is presented. The proposed method has been implemented in a field programmable gate array, to be used in real-time online applications. The algorithm obtained in average a 95% accuracy of failure detection.