Prediction method of deep transfer learning performance of permanent magnet synchronous motor under the condition of few-shot
Jin Liang1,2, Yan Yingang1,2, Yang Qingxin1, Liu Suzhen1, Zhang Chuang1
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300401 China; 2. Key Hebei Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300401 China
Abstract：Permanent magnet synchronous motor (PMSM) is a typical high-efficiency and energy-saving motor. The surrogate model can replace the numerical simulation represented by the finite element method to participate in the motor optimization design, which can improve the calculation efficiency of performance analysis and optimization and reduce resource consumption. It is the basis and focus of performance analysis and optimization. Using deep learning algorithm to establish surrogate model can achieve fast prediction and analysis of motor performance, but its training process requires a large number of samples. And the model prediction accuracy is low when the samples are insufficient, which restricts the application of the algorithm in engineering. Considering that the label samples accumulated in the historical task are sufficient, and there is relevant knowledge information between such samples and the samples of the target task, this paper establishes a deep transfer learning performance prediction method, applies the performance prediction knowledge transfer accumulated in the historical motor samples to the performance analysis of the target motor, and realizes the performance prediction task of the target motor under the condition of few-shot. In the field of electrical equipment performance prediction, there are many unlabeled feature samples and few labeled samples. We combine the deep belief network (DBN) with the model transfer method. DBN can self learn the characteristics of unlabeled samples in the target domain; model transfer can overcome the distribution difference between different data and solve the problem of scarcity of labeled samples in the target domain. Select the historical motor data set as the source domain and the target motor data set as the target domain. Firstly, the DBN is trained layer by layer unsupervised using the unlabeled samples of the target motor to obtain highly abstract important features. Secondly, a parameter sharing pre-training network is established by using the labeled samples of historical motors. Finally, by freezing the network, training the adaptation layer and fine-tuning the whole network, the model transfer is realized. And a deep transfer learning model that can effectively predict the target domain data is obtained. Taking 2004, 2010 and 2017 Prius vehicle PMSMs as the research object. Using two-dimension finite element analysis (2D-FEA) as the calculation tool, the data set is obtained by analyzing and calculating the motor output power and torque ripple under different design parameters. Considering the degree of structural difference between different motors, the case of similar motor group and differential motor group is verified, and the generalization ability of the model is further tested. Through the comparison and analysis with several models, the progressiveness and effectiveness of the method are verified. When only 5% labeled samples of the target motor are used, the mean absolute percentage error (MAPE) of output power and torque ripple for similar motor group are 1.54% and 2.77% respectively. Similarly, the MAPE values of output power and torque ripple are 2.73% and 4.37% respectively for differential motor group. When the precision is close to that of the traditional algorithm, the target domain label samples can be reduced by 35% - 55%. The results show that the proposed method can predict the performance of the target motor under the condition of few-shot.
金亮, 闫银刚, 杨庆新, 刘素贞, 张闯. 小样本条件下永磁同步电机深度迁移学习性能预测方法[J]. 电工技术学报, 0, (): 60-60.
Jin Liang, Yan Yingang, Yang Qingxin, Liu Suzhen, Zhang Chuang. Prediction method of deep transfer learning performance of permanent magnet synchronous motor under the condition of few-shot. Transactions of China Electrotechnical Society, 0, (): 60-60.
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