基于GAN的软测量缺失数据生成方法研究

Research on the generation method of missing data for soft measurement based on GAN

针对工业过程中传感器数据缺失造成软测量模型精度低的问题,提出一种基于生成对抗网络(generative adversarial nets,GAN)的传感器缺失数据生成方法。利用孤立森林算法检测出传感器数据的缺失区域;利用缺失数据属性特征训练条件生成对抗网络(conditional generative adversarial nets,CGAN),在CGAN的输入条件中添加随机序列作为附加信息迭代送入CGAN中生成数据,并借助WGAN-GP(wasserstein generative adversarial nets gradient penalty)成本函数提高网络训练的稳定性;针对缺失区域检测结果引入采样器,将采样的数据填补进缺失区域,形成完整数据集,以提高软测量模型精度。以镍闪速炉温度传感器数据为目标变量进行软测量建模,验证所提出的提高软测量模型精度方法的可行性与有效性。

To solve the problem of low precision in soft sensor models caused by sensor data loss in industrial processes,a new method of sensor data generation based on generative adversarial nets(GAN)is proposed.Firstly,the missing area of sensor data is detected by the isolated forest algorithm.Secondly,conditional generative adversarial nets(CGAN)are training using the attributes of missing data.By adding random sequences to the input conditions of CGAN as additional information,the data is generated iteratively in CGAN.The wasserstein generative adversarial nets gradient penalty(WGAN-GP)cost function is used to improve the stability of network training.Finally,a sampler is introduced to fill the sampled data into the missing region and form a complete data set to improve the accuracy of the soft sensing model.In this paper,the temperature sensor data of a nickel flash furnace is used as the target variable for soft-sensing modelling,and the feasibility and effectiveness of the proposed method to improve the accuracy of the soft-sensing model are verified.