Active learning strategy for smart soft sensor development under a small number of labeled data samples

• A new smart soft sensor has been developed based on the active learning strategy.
• Significant unlabeled data samples are selected through the smart active learning process.
• A lot of human resources could be saved with the development of the smart soft sensor.
• The superiority of the smart soft sensor is tested through an industrial process.

This contribution proposes a new active learning strategy for smart soft sensor development. The main objective of the smart soft sensor is to opportunely collect labeled data samples in such a way as to minimize the error of the regression process while minimizing the number of labeled samples used, and thus to reduce the costs related to labeling training samples. Instead of randomly labeling data samples, the smart soft sensor only labels those data samples which can provide the most significant information for construction of the soft sensor. In this paper, without loss of generality, the smart soft sensor is built based on the widely used principal component regression model. For performance evaluation, an industrial case study is provided. Compared to the random sample labeling strategy, both accuracy and stability have been improved by the active learning strategy based smart soft sensor.