Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.
The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.
Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help maintenance assistants on identifying faults in the power connections of induction machines. Specifically, a strategy for failure modes such as high resistance connections, single phasing faults and opposite wiring connections has been designed. In this case, as field data under the aforementioned faulty events are scarce in industry, a simulation-driven ML-based fault diagnosis strategy has been implemented. Hence, training data for the ML algorithm has been generated via Software-in-the-Loop simulations, to train the machine learning models.
During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade.
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.Sensors 2020, 20, 962 2 of 21 which normally requires an important effort in complex systems, but as an advantage, it is suitable for on-board and real-time implementation, as it requires neither a large quantity of data, nor high computational resources. The model-based techniques can be divided into two parts: the residual generation and the residual evaluation. On the other hand, data-driven techniques do not need a knowledge of the physical system, but the computational burden is normally higher. Moreover, data quantity to process and storage is large, so it makes difficult its on-board implementation.
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