基于生成对抗网络的热泵系统故障诊断数据集扩充方法研究

With the development of deep learning, more and more heat pump system fault diagnosis methods use deep learning technology and get good results. The fault diagnosis technology based on deep learning needs to rely on a large number of labeled fault data, but in reality, such data is very difficult to obtain, which limits the application of intelligent diagnosis technology. Aiming at this issue, the generative adversarial network (GAN) is proposed to learn the distribution of fault data and generate more labeled data to achieve the augmentation of the fault data set. For the complex operation data structure of the heat pump systems and the small difference of data value between different faults bring great difficulty to model learning, this paper proposes to use the heat pump system benchmark model to convert the operation data into residual data and use it as training data to reduce data complexity and increase the difference of data value. Using the MMD and 1-NN indicator to analyze the generated data, it is found that the distribution of the generated data is close to the real data, and the GAN model trained with residual data is of higher quality. Using the method of fault diagnosis to analyze the training results of models that use different amounts of generated data, it is found that the introduction of generated data can improve the accuracy of fault diagnosis under insufficient data conditions. The experimental results prove that the GAN-based data augmentation method can effectively reduce the dependence of intelligent diagnosis on labeled data, and has broad application prospects.