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Compact kernel distribution-based approach for broken bars detection on induction motors

Induction motors, important elements into the industry, are susceptible to faults during its lifetime service; yet, they can keep working without affecting the process, but increasing the production costs as they consume more electrical current. Broken rotor bars (BRB) detection is an important topic due to the fact that this failure is silent and produces a power consumption increasing, vibration, or introduction of spurious frequencies in the electric line, among others. In this regard, a monitoring system that can efficiently diagnose the induction motor condition is highly required. In this work, a new methodology for one and two broken bars detection is presented. First, the compact kernel distribution (CKD) algorithm, a new high resolution time-frequency algorithm, is introduced for the detection of anomalies produced by the BRB failure in the startup current signal by considering that these signals describe changes on its dynamic characteristics due to the fault; then, the variance, a statistical feature, of the signal processed by CKD determines in automatic way the induction motor condition. The obtained results show a high overall efficiency for detecting broken rotor bars as well as healthy condition.