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

doi:10.16183/j.cnki.jsjtu.2020.086

Abstract

Abstract:

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.

Key words: interpretability, neural network, soft-sensing method, compressor

CLC Number:

TK315

WANG Yulin, ZHOU Dengji, HAO Jiarui, HUANG Dawen. A Compressor Power Soft-Sensing Method Based on Interpretable Neural Network Model[J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 774-780.

Figures/Tables 9

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训练样本	模型A		模型B		模型C
训练样本	RMSE/%	MAE/%	RMSE/%	MAE/%	RMSE/%	MAE/%
数据集1	0.95	0.83	0.99	0.81	0.94	0.49
数据集2	6.35	5.27	2.14	1.94	1.28	0.73