Year: 2025
Type: conference-paper
Abstract: 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 physics-based 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.
Institution: Mondragon Unibertsitatea
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411205319