一种基于可解释神经网络模型的压缩机功率软测量方法

In order to ensure the accuracy and efficiency of measurement, and reduce the dependence of the soft sensing on dataset, a soft-sensing method of compressor power based on interpretable neural network is proposed. When training on a dataset with good generalization in the experiment, the root mean squared error(RMSE) of the interpretable neural network model on the test set is 0.0094, which is 1.1% lower than that of the back propagation(BP) neural network model. When training on a dataset with poor generalization, the RMSE of the interpretable neural network model on the test set is 0.0128, which is 79.8% lower than that of the BP neural network model. The experimental results show that the soft-sensing method based on interpretable neural network not only has a high accuracy rate, but also can maintain a good measurement performance when training on a dataset with poor generalization.

Keywords： interpretability; neural network; soft-sensing method; compressor