针对现有DC/DC电路故障预测中以距离作为故障特征参数存在的不足, 提出了将分段动态时间弯曲距离作为DC/DC电路故障特征参数, 并将此特征参数与长短时记忆神经网络(Long Short-Term Memory,LSTM)算法相结合, 对DC/DC电路进行故障预测。首先, 选择电路输出电压作为监测信号; 然后,利用动态时间弯曲算法计算其与无故障输出电压的分段动态时间弯曲距离作为电路的故障特征参数; 最后, 基于LSTM预测模型实现电路故障特征参数时间序列预测。以Boost电路为例进行了仿真实验, 验证了该方法的有效性和准确性。

Aiming at the deficiencies existing in the DC/DC circuit fault prediction with distance as the fault characteristic parameter, the Piecewise Dynamic Time Warping (P_DTW)distance is proposed as the fault characteristic parameter of DC/DC circuit, which is used together with the Long Short-Term Memory (LSTM) algorithm for the fault prediction of DC/DC circuits. First, the circuit output voltage is chosen as the monitoring signal. Then, the dynamic time warping algorithm is used to calculate its P_DTW distance with the faultless output voltage, which is used as the fault characteristic parameter of the circuit. Finally, the time series prediction of circuit fault characteristic parameters is implemented based on the LSTM prediction model.The Boost circuit is taken as an example to verify the effectiveness and accuracy of the proposed method.