基于数据驱动的牵引逆变器电压传感器间歇性故障诊断方法

Data-driven Diagnosis Method for Voltage Sensor Intermittent Faults of Traction Inverter System

由于间歇性故障具有随机性与不可预测性,传统的故障诊断方法难以适用于间歇性故障的诊断。为了提高牵引逆变系统的可靠性,文中提出一种针对直流侧电压传感器间歇性故障的智能诊断方法。首先,结合非线性自回归动态网络结构与极限学习机快速挖掘历史数据与异步电机定子电流之间的非线性映射关系,得到牵引电机定子电流预测器;随后,设计滑动时间窗口构建电流残差,检测间歇性故障的出现与消失时间,以此获取表征间歇性故障严重程度的评价指标。所提方法基于快速控制原型实验平台完成实验验证,结果表明,电流预测器对负载突变、速度突变等动态工况具有良好鲁棒性,提出的诊断方法可分别在0.65、0.9 ms内完成间歇性故障出现时间与消失时间的检测,且可准确辨识传感器间歇性故障的早期、中期、晚期阶段,实现了传感器间歇性故障严重程度的评价。

Due to the randomness and unpredictability of intermittent faults,traditional fault diagnosis methods are difficult to apply to the diagnosis of intermittent faults.In order to improve the reliability of the traction inverter system,this paper proposes an intelligent diagnosis method for the intermittent faults of the DC side voltage sensor.First,combining the nonlinear autoregressive dynamic network structure and the extreme learning machine to quickly mine the nonlinear mapping relationship between historical data and the stator currents of the asynchronous motor,the stator currents predictor of the traction motor is obtained.Then,a sliding time window is designed to construct the currents residuals,and the occurrence and disappearance time of intermittent faults are detected to obtain the evaluation index that characterizes the severity of intermittent faults.The proposed method is verified based on the rapid control prototype(RCP)experimental platform.The results show that the current predictor has great robustness to dynamic conditions such as sudden load changes and sudden speed changes.The proposed diagnosis method can complete the detection of the occurrence times and disappearance times of intermittent faults within 0.65 ms and 0.9 ms,respectively,and can accurately identify the early,middle,and late stages of intermittent faults of sensors,realizing the evaluation of the severity of intermittent faults.