2026-04-07T11:47:16Zhttps://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

00925njm 22002777a 4500 dc Gonzalez-Jimenez, David author del-Olmo, Jon author Poza, Javier author Sarasola, Izaskun author Cabezas Olivenza, Mireya author 2025 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. 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 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 Hybrid Fault Detection and Diagnosis Approach of Power Connections for Induction Motors