Xu Xiaowei, Feng Jingyi, Zhan Liu, Li Zhixiong, Qian Feng, Yan Yunbing
School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.
Entropy (Basel). 2021 Mar 12;23(3):339. doi: 10.3390/e23030339.
As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.
作为一个由电气、磁和热机组成的复杂场路耦合系统，电动汽车的永磁同步电动机具有多种运行工况和复杂的工况环境。存在各种形式的故障，且故障征兆相互交叉或重叠。随机性、继发性、并发和关联特性使得故障诊断困难。同时，常见的智能诊断方法准确率低、泛化能力差且难以处理高维数据。本文提出一种基于堆叠去噪自动编码器（SDAE）原理结合支持向量机（SVM）分类器的电机故障特征提取方法。首先，对实验采集的电机信号进行处理，通过添加噪声对输入数据进行随机损坏。此外，根据实验结果构建堆叠去噪自动编码器的网络结构，设置最优学习率、降噪系数等网络参数。最后，利用训练好的网络对测试样本进行验证。与传统故障提取方法和单自动编码器方法相比，该方法具有准确率更高、泛化能力强以及易于处理高维数据特征等优点。
