A Compressor Power Soft-Sensing Method Based on Interpretable Neural Network Model

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 0.009 4, 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 0.012 8, 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. © 2021, Shanghai Jiao Tong University Press. All right reserved.