Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2021 , Vol. 55 >Issue 6: 774 - 780

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.086

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

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  • School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2020-04-03

  Online published: 2021-06-08

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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

Cite this article

WANG Yulin, ZHOU Dengji, HAO Jiarui, HUANG Dawen . A Compressor Power Soft-Sensing Method Based on Interpretable Neural Network Model[J]. Journal of Shanghai Jiaotong University, 2021 , 55(6) : 774 -780 . DOI: 10.16183/j.cnki.jsjtu.2020.086

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