Abstract
Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. 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 time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance 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.
Highlights
During the last decade, maintenance in railway systems has started moving from corrective and time-based preventive maintenance to condition-based maintenance (CBM)
The evaluation is normally done by onboard data storage, observer-based approaches are more suitable for onboard implementation in the mean of norms for signals [26], statistical methods as the Generalized Likelihood Ratio (GLR) [27] or machine learning [28], which traction control unit
The strategy aims to get detailed and quantitative information related to the fault mode, which allows a quick maintenance action or an automatic reconfiguration of the sensor, in order to minimize the effects of the fault in
Summary
Maintenance in railway systems has started moving from corrective and time-based preventive maintenance to condition-based maintenance (CBM). In contrast to Fault Detection and Isolation (FDI) strategies that are limited to fault detection and location, FDD provides the severity of the fault [4], which can be important information for maintenance decision-making, in order to schedule the maintenance task, depending on the priority level and system degradation [5]. Information related to severity and fault mode could be essential to get an optimal fault tolerant system, by means of parameters change or reconfigurations in an automatic way, for example by substituting the measured value by the estimated value [6]
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