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An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes
Author(s) -
Geng Zhiqiang,
Chen Ning,
Han Yongming,
Ma Bo
Publication year - 2020
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.23674
Subject(s) - warning system , process (computing) , computer science , alarm , false alarm , constant false alarm rate , fault detection and isolation , early warning system , principal component analysis , automation , data mining , artificial intelligence , real time computing , engineering , telecommunications , actuator , aerospace engineering , operating system , mechanical engineering
With the development of industrial automation, the requirement of abnormal early warning in the industrial production process is getting higher and higher. Facing complex chemical processes, traditional fault detection and abnormal early warning methods have low detection efficiency and poor real‐time performance. Therefore, this paper analyzes and studies fault detection and abnormal early warning methods, and puts forward an improved intelligent early warning method based on the moving window sparse principal component analysis (MWSPCA) suitable for complex chemical processes. The sparse principal component analysis algorithm is used to establish the initial early warning model, and then the moving window is used to update the early warning model, which makes the early warning model more suitable for the characteristics of time‐varying data. Furthermore, the proposed method reduces the false alarm rate and missed alarm rate of the early warning, and improves the real‐time performance of the early warning model. Finally, the feasibility and the validity of the proposed method are verified by the TE process and the oil drilling process. The experiment results show that the proposed method can reduce the risk of complex chemical processes.