期刊
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
卷 11, 期 4, 页码 992-1003出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2013.2288279
关键词
Case feature weighting; case-based reasoning (CBR); data-driven; fuzzy-similarity rough sets (FSRS); product quality estimation; soft-sensor modeling
资金
- National Natural Science Foundation of China [61104084, 61290323, 61333007, 61304107]
- 111 Project [B08015]
- IAPI Fundamental Research Funds [2013ZCX02-09]
- Guangdong Education University-Industry Cooperation Projects [2010B090400410]
Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor (k-NN) algorithm is strongly influenced by the value of, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability.
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