Synthesizing labeled data to enhance soft sensor performance in data-scarce regions. (October 2021)

• Record Type: Journal Article
• Authors: Lyu, Yuting; Chen, Junghui; Song, Zhihuan
• Is Part Of: Control engineering practice. Volume 115(2021)
• Publication Date: 2021-10
• Subjects: Soft sensor -- Variational autoencoder -- Deep learning -- Data synthesis; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104903
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Quality variables are key indicators of the operating performance in industrial processes. Because they are difficult to measure, soft sensor models can be adopted to predict them timely. For accurate prediction, sufficient training data are necessary to construct a good soft sensor model. In practical industrial processes, however, data labeled with quality variables are usually deficient in the desired region. Particularly, when the process is just switched to a new mode, available data in this new mode are initially quite a few. In this paper, a novel data synthesis method based on the regressor-embedded semi-supervised variational autoencoder (RSSVAE) model is proposed to generate synthetic labeled data when the original labeled data are inadequate. The proposed model utilizes not only the original data in the data-scarce region but also the data in other regions, which share some common information with the scarce data. Meanwhile, data synthesis and model correction mechanism are implemented iteratively to avoid model biases. Once the synthetic labeled data of the data-scarce region are acquired, they are combined with the original labeled data to establish a local soft sensor and predict the quality variables of the unlabeled data. Finally, a real ammonia synthesis process is introduced to demonstrate the effectiveness of the proposed method. Highlights: A new model called RSSVAE is proposed. RSSVAE uses the data of data-intensive regions to synthesize data. The synthetic data generated by RSSVAE are used for the data-scare region. A model correction mechanism is proposed to avoid model biases. The proposed method is evaluated in a real ammonia synthesis process.