Hardware‐in‐the‐loop performance analysis of a railway traction system under sensor faults

Garramiola, Fernando; Poza, Javier; Olmo, Jon del; Nieva, Txomin

doi:10.1049/joe.2018.8249

Cited by 6 publications

(4 citation statements)

References 14 publications

(12 reference statements)

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“…However, to inject the faults, our framework is based on manipulating the signals accessed on the CAN bus in accordance with the user’s specifications in terms of the location, time and type. Concerning the same area, to evaluate the risks in railway traction drive and to analyze its behavior, an improvement of FMEA using a HiL-based FI approach was proposed in [ 53 ]. In the proposed research, the focus was on improving the FMEA methodology to provide a quantitative analysis using FI with the purpose of creating failure scenarios.…”

Section: Background and Related Workmentioning

confidence: 99%

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Abboush

Bamal

Knieke

et al. 2022

Sensors

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A well-known challenge in the development of safety-critical systems in vehicles today is that reliability and safety assessment should be rigorously addressed and monitored. As a matter of fact, most safety problems caused by system failures can lead to serious hazards and loss of life. Notwithstanding the existence of several traditional analytical techniques used for evaluation based on specification documents, a complex design, with its multivariate dynamic behavior of automotive systems, requires an effective method for an experimental analysis of the system’s response under abnormal conditions. Simulation-based fault injection (FI) is a recently developed approach to simulate the system behavior in the presence of faults at an early stage of system development. However, in order to analyze the behavior of the system accurately, comprehensively and realistically, the real-time conditions, as well as the dynamic system model of the vehicle, should be considered. In this study, a real-time FI framework is proposed based on a hardware-in-the-loop (HiL) simulation platform and a real-time electronic control unit (ECU) prototype. The framework is modelled in the MATLAB/Simulink environment and implemented in the HiL simulation to enable the analysis process in real time during the V-cycle development process. With the objective of covering most of the potential faults, nine different types of sensor and actuator control signal faults are injected programmatically into the HiL system as single and multiple faults without changing the original system model. Besides, the model of the whole system, containing vehicle dynamics with the environment system model, is considered with complete and comprehensive behavioral characteristics. A complex gasoline engine system is used as a case study to demonstrate the capabilities and advantages of the proposed framework. Through the proposed framework, transient and permanent faults are injected in real time during the operation of the system. Finally, experimental results show the effects of single and simultaneous faults on the system performance under a faulty mode compared to the golden running mode.

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Section: Background and Related Workmentioning

confidence: 99%

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Abboush

Bamal

Knieke

et al. 2022

Sensors

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“…The threshold for both sensors is set based on the minimum fault to detect, and taking into account that the threshold should be over the maximum value of the GLR for the different residuals, obtained in sensor fault-free simulations, as the false positive ratio should be minimized. Furthermore, the minimum fault to detect is set based on the quantitative effects of the sensor fault in the system, obtained by means of fault injections and dynamic simulation [32], which provides the maximum values of residuals for sensor fault-free scenarios.…”

Section: Fault Evaluation Based On Generalized Likelihood Ratiomentioning

confidence: 99%

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Garramiola

Poza

Madina

et al. 2020

Sensors

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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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“…In AC systems, the ground fault on lines [6], open-circuit fault in converters [7] as well as internal faults in transformers [8], sensors [9] and motors [10] have been researched. While defects on lines [11] and gain fault in sensors [12] for the DC system have been discussed. In [13], the negative mutual impact between AC and DC system under parallel operation has been analyzed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid

Liu

Liang

Dinavahi

2020

IEEE Trans. Power Delivery

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Reliable and comfortable high-speed railway (HSR) has skyrocketed in popularity as a transportation medium for traveling around the world. High-voltage direct current (HVDC) electrification system has been introduced to the HSR gradually. However, the coexistence of AC and DC systems will last for a long time because AC railway systems are still in the dominant position. A detailed HSR traction system transient model operating under the hybrid AC/DC grid was established in PSCAD/EMTDC. We proposed a real-time fault detection and isolation (FDI) method for the simulated model using neural network (NN). Hierarchical structure of the monitoring system has been employed. Low-level sub-monitors supervised the conditions of their local regions and the top-level monitor collected all the feedback from sub-monitors making the final evaluation of the entire HSR system based on a voting strategy. Both off-line and real-time experiments were conducted to validate the effectiveness of the proposed method. In the experiments, the sub-monitors were designed based on Gated Recurrent Unit (GRU) algorithm and implemented on the Xilinx VCU128 FPGA board. For the off-line experiment, the sub-monitors used the training and testing dataset both from PSCAD/EMTDC to construct the architecture of their individual GRU networks and to verify how great the networks can be. For the real-time task, the sub-monitors interfaced with a real-time HSR system emulator running on the Xilinx VCU118 FPGA board to test the performance in the real-time application. The results proved that our proposed FDI method has the capability of real-time detection and can achieve better accuracy within reasonable time and resource consumption than other NN-based methods. Moreover, the method was capable of standing against noises from measured signals to some extent. Index Terms-Data-driven methods, fault detection and isolation (FDI), field-programmable gate array (FPGA), Gated Recurrent Unit (GRU), high-speed railway (HSR), high-voltage direct current (HVDC), hybrid AC/DC grid, long short-term memory (LSTM), machine learning, neural networks, real-time systems.

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Hardware‐in‐the‐loop performance analysis of a railway traction system under sensor faults

Cited by 6 publications

References 14 publications

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid

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