Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning

doi:10.1007/s12204-015-1606-y

Abstract

Abstract: Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning (SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

Key words: soft-sensing|semi-supervised learning (SSL)| online correction| neural network

摘要： Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning (SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

关键词: soft-sensing|semi-supervised learning (SSL)| online correction| neural network

CLC Number:

TP 274.2

TANG Qi-feng (汤奇峰), LI De-wei* (李德伟), XI Yu-geng (席裕庚). Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning[J]. Journal of shanghai Jiaotong University (Science), 2015, 20(2): 171-176.

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