摘要
针对电源车健康维护存在的问题,提出了一种基于长短时间记忆LSTM(Long Short Time Memory)网络与序贯概率比检验SPRT(Sequential Probability Ratio Test)融合的电源车故障诊断方法。该方法基于LSTM网络建立电源车的多变量时间序列模型,并引入SPRT方法进行自适应多样本故障诊断。经在电源车仿真系统上进行对比实验,结果表明LSTM诊断模型有更强的学习和映射能力,LSTM-SPRT融合的故障诊断方法,显著提高了电源车故障诊断的准确率和可靠性。
Focus on the health maintenance of vehicle power supply, a fault diagnosis method of vehicle power supply is proposed, which is based on the long and short time memory LSTM(Long Short Time Memory) network and the sequential probability ratio test SPRT(Sequential Probability Ratio Test). Based on the LSTM network, the multivariate time series model of vehicle power supply is established, and the SPRT method is used to perform the adaptive multi-sample fault diagnosis. The experiment on the vehicle power supply simulation system shows that the LSTM diagnosis model has stronger learning and mapping capabilities, and the fault diagnosis method based on the LSTM-SPRT fusion significantly improves the accuracy and reliability of the vehicle power supply fault diagnosis.
作者
李炜
周丙相
蒋栋年
Li Wei;Zhou Bingxiang;Jiang Dongnian(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,China;National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2020年第4期638-648,共11页
Journal of System Simulation
基金
国家自然科学基金(61763027)
甘肃省自然科学基金(1610RJTA022)。
关键词
长短时间记忆网络
序贯概率比检验
电源车仿真系统
故障诊断
long short-time memory network
sequential probability ratio test
vehicle power supply simulation system
fault diagnosis
