2026-04-07T11:45:58Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/140082026-01-29T08:38:31Zcom_20.500.11984_1143col_20.500.11984_1148

Hybrid Fault Detection and Diagnosis Approach of Power Connections for Induction Motors Gonzalez-Jimenez, David del-Olmo, Jon Poza, Javier Sarasola, Izaskun Cabezas Olivenza, Mireya Data-driven methods fault diagnosis Hardware Physics-based model (PBM) ODS 7 Energía asequible y no contaminante ODS 9 Industria, innovación e infraestructura ODS 11 Ciudades y comunidades sostenibles The increasing demand for reliable electric mobility solutions highlights the need for advanced fault detection and diagnosis (FDD) strategies to ensure system reliability and safety. This paper presents a hybrid approach that integrates physicsbased models with data-driven techniques for FDD in induction motors (IMs). A Hardware-in-the-Loop (HiL) platform is used to generate synthetic data that replicates real-life operating conditions. In this study, a progressive classification strategy based on a One-Class Support Vector Machine (OCSVM) is trained exclusively on healthy operation data and tested with HiL-generated faulty responses to evaluate its anomaly detection capabilities. Focusing on railway traction systems, the research simulates common IM faults, including opposite-phase wiring, high-resistive connections, and open-phase failures. By extending the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology with physics-based model validation and synthetic data generation, the proposed hybrid strategy enhances scalability, effectively addressing challenges such as limited faulty data and inadequate real-time monitoring. This approach demonstrates significant potential for improving fault detection in electric traction applications. 2025-12-16T10:05:00Z 2025-12-16T10:05:00Z 2025 979-8-3315-2075-5 2694-0264 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=200410 https://hdl.handle.net/20.500.11984/14008 eng © 2025 IEEE IEEE