Kılıçkaya, SertaçEren, Levent2025-01-252025-01-2520250948-79211432-0487https://doi.org/10.1007/s00202-024-02764-3https://hdl.handle.net/20.500.14365/5843Adjustable speed drives (ASDs) are widely used in industry for controlling electric motors in applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of ASD-fed induction machines is very critical for preventing failures. Motor current signature analysis offers a non-invasive approach to assess motor condition. Application of conventional convolutional neural networks provides good results in detecting and classifying fault types for utility line-fed motors, but the accuracy drops considerably in the case of ASD-fed motors. This work introduces the use of self-organized operational neural networks to enhance the accuracy of detecting and classifying bearing faults in ASD-fed induction machines. Our approach leverages the nonlinear neurons and self-organizing capabilities of self-organized operational neural networks to better handle the non-stationary nature of ASD operations, providing more reliable fault detection and classification with minimal preprocessing and low complexity, using raw motor current data. © The Author(s) 2024.eninfo:eu-repo/semantics/openAccessBearing Fault DetectionCondition MonitoringMotor Current Signature AnalysisOperational Neural NetworkArticle10.1007/s00202-024-02764-32-s2.0-105003421512