@inproceedings{e6180b1ef1fc4a01b38d07dc380589e9,
title = "Research and application of KICA-AROMF based fault diagnosis",
abstract = "With the development of the modern industrial system, data-driven fault diagnosis methods have attracted more and more attention. Fault diagnosis of complex industrial processes based on one-dimensional adaptive rank-order morphological filter (AROMF) may miss key information because of excessive dimension reduction of process data. To solve this problem, a method combining the kernel independent component analysis (KICA) with one-dimensional AROMF is proposed. Firstly, KICA is used for nonlinear feature extraction, getting the template signal and the test signal of each pattern. Then, a fault diagnosis method via multi-dimensional signals classification method based on AROMF is presented in this paper. The advantage of the proposed method was confirmed by the simulation of the Tennessee Eastman process.",
author = "Zhu, \{Qun Xiong\} and Meng, \{Qian Qian\} and Yuan Xu and He, \{Yan Lin\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th International Symposium on Advanced Control of Industrial Processes, AdCONIP 2017 ; Conference date: 28-05-2017 Through 31-05-2017",
year = "2017",
month = jul,
day = "18",
doi = "10.1109/ADCONIP.2017.7983783",
language = "English",
series = "2017 6th International Symposium on Advanced Control of Industrial Processes, AdCONIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "215--220",
booktitle = "2017 6th International Symposium on Advanced Control of Industrial Processes, AdCONIP 2017",
address = "United States",
}