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A data‐driven fault detection approach with performance optimization
Author(s) -
Li Linlin,
Ding Steven X.,
Peng Kaixiang,
Han Huayun,
Yang Ying,
Yang Xu
Publication year - 2018
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22994
Subject(s) - kernel (algebra) , realization (probability) , fault detection and isolation , representation (politics) , computer science , fault (geology) , gradient descent , scheme (mathematics) , data driven , external data representation , algorithm , kernel method , decomposition , artificial intelligence , mathematics , mathematical analysis , ecology , statistics , combinatorics , seismology , politics , political science , artificial neural network , support vector machine , law , actuator , biology , geology
This paper is concerned with the data‐driven realization of fault detection approach with performance optimization. For our purpose, the data‐driven realization form of linear kernel representations is studied first, which is essential in our work. It is followed by a data‐driven realization of kernel representation and its implementation in the design scheme of fault detection systems. Nevertheless, the basic idea behind this approach lies in the one‐step identification of kernel representation using LQ‐decomposition. Then, the recursive kernel representation is introduced and the so‐called gradient descent algorithm is applied to optimize the performance of the proposed data‐driven fault detection system. The effectiveness of the proposed approaches is illustrated by a numerical example and a case study on laboratory setup of a three‐tank system.