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基于改進多模型FDA的間歇生產(chǎn)過程的故障診斷
Abstract：Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its operation. In order to overcome the need for estimated or filled up future unmeasured values in the online fault diagnosis, sufficiently utilize the finite information of faults, and enhance the diagnostic performance, an improved multi-model Fisher discriminant analysis is represented. The trait of the proposed method is that the training data sets are made of the current measured information and the past major discriminant information, and not only the current information or the whole batch data. An industrial typical multi-stage streptomycin fermentation process is used to test the performance of fault diagnosis of the proposed method. 作者： 蔣麗英 謝磊 王樹青 Author： JIANG Liying XIE Lei WANG Shuqing 作者單位： 浙江大學 期 刊： 中國化學工程學報（英文版） ISTICEISCI Journal： CHINESE JOURNAL OF CHEMICAL ENGINEERING 年，卷(期)： 2006, 14(3) 分類號： N94 Keywords： fault diagnosis Fisher discriminant analysis batch processes 機標分類號： TP3 S63 機標關鍵詞： 多模型 間歇 生產(chǎn)過程 故障診斷 Discriminant Analysis fault diagnosis discriminant analysis fermentation process batch processes diagnosis of 基金項目： 國家自然科學基金 基于改進多模型FDA的間歇生產(chǎn)過程的故障診斷[期刊論文] 中國化學工程學報（英文版） --2006, 14(3)蔣麗英 謝磊 王樹青Since there are not enough fault data in historical data sets, it is very difficult to diagnose faults for batch processes. In addition, a complete batch trajectory can be obtained till the end of its ...【基于改進多模型FDA的間歇生產(chǎn)過程的故障診斷】相關文章：
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