2026-04-30T13:55:53Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/15942024-03-04T13:54:34Zcom_20.500.11984_473col_20.500.11984_478

Garramiola, Fernando Poza, Javier Madina, Patxi del-Olmo, Jon Ugalde, Gaizka 2020-03-25T15:26:10Z 2020-03-25T15:26:10Z 2020 1424-8220 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=155348 https://hdl.handle.net/20.500.11984/1594 Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order tomove frompreventivemaintenance to condition-basedmaintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a timewindowof sensormeasurements and sensor fault reconstruction is sent to the remotemaintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform. eng Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ © by the authors fault diagnosis railway model-based approach data-driven approach sliding mode observer sensor fault reconstruction condition-based maintenance A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives http://purl.org/coar/resource_type/c_6501