Eren L.Devecioglu O.C.İnce, TürkerAskar M.2023-06-162023-06-1620229.78E+12https://doi.org/10.1109/IECON49645.2022.9968348https://hdl.handle.net/20.500.14365/355948th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 -- 17 October 2022 through 20 October 2022 -- 184962Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessBroken rotor bar detectioninduction motorsoperational neural networksComputer aided diagnosisConvolutional neural networksCost effectivenessFault detectionLearning systemsBar detectionBroken rotor barBroken rotor bar detectionConvolutional neural networkInductions motorsMachine learning techniquesMechanical faultsNeural-networksOperational neural networkSelf-organisedInduction motorsConference Object10.1109/IECON49645.2022.99683482-s2.0-85143895948