基于新型深度神经网络的汽车传动故障诊断

Q2 Engineering
Cangku Guo
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引用次数: 0

摘要

时代在进步。面对越来越多的电动汽车,他们使用动力电池作为储能电源。驱动电机作为电动汽车的核心部件,关系到车辆的正常运行。如果驱动电机发生故障,乘客可能会受到不可逆转的伤害,因此对电动汽车驱动电机的诊断非常重要。本文主要对电动汽车的故障进行分析,并利用诊断信号对故障进行诊断。提出了一种基于深度神经网络的汽车驱动故障诊断方法。该方法构建了CNN-LSTM模型。首先通过快速傅里叶变换将振动信号转换为时频图像,然后将时频图像输入到所提出的模型中,得到故障分类结果。此外,还引入了CNN、LSTM和BP神经网络与本文提出的方法进行了比较。结果表明,CNN-LSTM模型在汽车驱动故障诊断中优于其他三种模型,故障准确率达到99.02%,表现出优异的故障诊断性能。在使用相同学习率进行训练时,基于CNN-LSTM的车辆驱动故障诊断方法的损失减少率明显优于其他三种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault diagnosis of automobile drive based on a novel deep neural network
Abstract The times are progressing. Facing the increasing number of electric vehicles, they use power batteries as energy storage power sources. As a core component of electric vehicle, the drive motor is related to the normal operation of the vehicle. If the driving motor fails, passengers may be irreversibly hurt, so it is very important to diagnose the driving motor of electric vehicle. This paper mainly analyzes the faults of electric vehicles, and makes use of diagnostic signals to diagnose the faults. A novel fault diagnosis method of automobile drive based on deep neural network is proposed. In this method, CNN-LSTM model is constructed. Firstly, the vibration signals are transformed into time-frequency images by fast Fourier transform, and then the time-frequency images are input into the proposed model to obtain the fault classification results. In addition, CNN, LSTM and BP neural network are introduced to compare with the methods proposed in this paper. The results show that CNN-LSTM model is superior to the other three models in the fault diagnosis of automobile drive, reaching 99.02 % of the fault accuracy rate, showing excellent fault diagnosis performance. And when the same learning rate is used for training, the rate of loss reduction is obviously better than that of the other three types of vehicle drive fault diagnosis method based on CNN-LSTM.
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来源期刊
Energy Harvesting and Systems
Energy Harvesting and Systems Energy-Energy Engineering and Power Technology
CiteScore
2.00
自引率
0.00%
发文量
31
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