FIGAN: A Missing Industrial Data Imputation Method Customized for Soft Sensor Application

Missing data is quite common in the industrial field, resulting in problems in downstream applications, as most data driven methods used in these applications rely on complete and high-quality dataset to build a high-quality model. Existing methods deal with missing data individually regardless of its downstream application, treating all variables equally without considering their different roles in the downstream application. This would affect imputation performance for key variables, thus deteriorating the accuracy of the downstream model. A considerable challenge is how to refine the missing data imputation task. In this paper, a new method termed fine-tuned imputation GAN (FIGAN) is designed to achieve customized data imputation for industrial soft sensor. The major contribution of the paper lies in two aspects: 1) different from the original imputation GAN (GAIN) which treats all variables equally, FIGAN is guided by a soft sensor module so as to achieve customized data imputation by performing improved data imputation on quality-related variables. Enhanced accuracy for the final industrial soft sensor would be possible; 2) in addition, since labels of the soft sensor might also have missing data, a soft sensor with pseudo labeling is designed to conquer the problem with data imputation and label prediction being optimized interactively. Case studies on a converter steelmaking process and a penicillin fermentation process show the feasibility of the proposed FIGAN. It is noted that such customized imputation could be readily transferred to other downstream applications with missing data.

Automated summary

Missing data is common in the industrial field, leading to problems in downstream applications. The new method FIGAN is designed to achieve customized data imputation for industrial soft sensor, guided by a soft sensor module and pseudo labeling. This method shows improved accuracy for the final industrial soft sensor and can be readily transferred to other applications with missing data.