Assessment of operating performance using cross-domain feature transfer learning. (August 2019)

• Record Type: Journal Article
• Authors: Zou, Xiaoyu; Wang, Fuli; Chang, Yuqing
• Is Part Of: Control engineering practice. Volume 89(2019)
• Page Start: 143
• Page End: 153
• Publication Date: 2019-08
• Subjects: Assessment of operating performance -- Transfer learning -- Cross-domain learning -- Feature learning -- Copper flotation; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2019.05.007
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Assessment of operating performance evaluates optimal degree for processes under normal conditions. Maintaining in good operating performance indicates high benefit of the production. To assess the performance for a newly built process with a small number of historical data, a cross-domain feature transfer learning (CDFTL) method is proposed in this research. The assumption that the data from the source and target domains should be independent and identically distributed is no longer necessary. The common and specific features are both utilized to guarantee the assessment precision and model generalization ability. Finally, the proposed method is applied to a copper flotation process.