Mode-cloud data analytics based transfer learning for soft sensor of manufacturing industry with incremental learning ability. (May 2020)

• Record Type: Journal Article
• Authors: Wang, Jie; Zhao, Chunhui
• Is Part Of: Control engineering practice. Volume 98(2020)
• Publication Date: 2020-05
• Subjects: Soft sensors -- Data analytics -- Transfer learning -- Incremental learning -- Mode-cloud -- Manufacturing industry; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104392
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In modern manufacturing enterprises, quality-related soft sensors are important in production, especially for batch manufacturing processes. In practice, batch processes frequently produce new process modes due to various factors, such as changes in operating recipes, uncertainty in the external environment, and various product specifications. When this is the case, directly applying the historical model may lead to unexpected results. Moreover, it may be impractical to conduct enough trial runs and wait until sufficient batches are available before modeling for the new mode. Therefore, the problem of modeling for a new process mode with insufficient data brings challenges for the soft-sensor issue, which has rarely been addressed before. To solve the problem as mentioned above, a novel transfer and incremental soft-sensor scheme is developed for batch manufacturing processes with the support of multiple historical process modes, termed mode-cloud here. Using the proposed algorithm, the production data from the cloud of historical modes can be explored and utilized regarding their different phase-based relationships between process variables and product qualities. Thus, a new objective function is constructed to automatically identify and quantify the information buried in the vast ocean of historical data to determine the initial soft-sensor model for the new mode with limited data. Besides, with the constant increase of new samples, the initial soft-sensor model can incrementally update model parameters and release the workload of repetitive modeling. With both transfer modeling and incremental updating abilities, the proposed algorithm, which we call mode-cloud based transfer incremental learning (MTIL), can not only offer high adaptability and flexibility to accommodate a new process mode quickly, but also ensure the prediction accuracy. The MTIL based soft-sensor scheme is applied to the real injection molding process for the illustration purpose. Highlights: It proposes a new soft-sensor method with transfer and incremental learning ability. A transfer soft-sensor model is proposed for the new mode with limited data. The modeling procedures are speeded up and prediction performance is enhanced. The inccremental updating enables parameters optimization without repetitive modeling.