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首页> 外文期刊>Electrical Systems in Transportation, IET >Artificial neural network-based fault diagnosis in the AC–DC converter of the power supply of series hybrid electric vehicle
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Artificial neural network-based fault diagnosis in the AC–DC converter of the power supply of series hybrid electric vehicle

机译：串联混合动力汽车电源AC-DC变换器中基于人工神经网络的故障诊断

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摘要

AC–DC converter switches of the drive train of series hybrid electric vehicles (SHEVs) are generally exposed to the possibility of outbreak open-phase faults because of troubles with the switching devices. In this framework, the present study proposes an artificial neural network (ANN)-based method for fault diagnosis after extraction of a new pattern. The new pattern under AC–DC converter failure in view of SHEV application has been used for train-proposed ANN. To achieve this goal, four different levels of switches fault are considered on the basis of both simulation and experimental results. Ensuring the accuracy and generalisation of the introduced pattern, several parameters have been considered, namely: capacitor size changes, load, and speed variations. The experimental results validate the simulation results thoroughly.
机译：混合动力电动汽车（SHEV）的传动系统的AC-DC转换器开关通常会因开关设备的故障而出现爆发性开路故障的可能性。在此框架下，本研究提出了一种基于人工神经网络（ANN）的方法，用于在提取新模式后进行故障诊断。考虑到SHEV的应用，AC-DC转换器故障下的新模式已用于列车建议的ANN。为了实现这一目标，在仿真和实验结果的基础上考虑了四个不同级别的开关故障。为确保所引入模式的准确性和通用性，已考虑了几个参数，即：电容器尺寸变化，负载和速度变化。实验结果充分验证了仿真结果。

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