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A Dynamic Independent Component Analysis Approach To Fault Detection With New Statistics

M. Teimoortashloo1, A. Khaki Sedigh2,*

1 Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Tel.: +989153196220, Fax: +985842249420, E-mail address: Address: Ghiyam, Number 409, Bojnourd, Iran
2 Industrial Control Center of Excellence, Department of Electrical and Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran

* Corresponding Author: E-mail address: , Tel.: +982184062317 Address: Sayyed Khandan, P.O.Box 16315-1355 Tehran, Iran

Computer Systems Science and Engineering 2018, 33(1), 5-20.


This paper presents a fault detection method based on Dynamic Independent Component Analysis (DICA) with new statistics. These new statistics are statistical moments and first characteristic function that surrogate the norm operator to calculate the fault detection statistics to determine the control limit of the independent components (ICs). The estimation of first characteristic function by its series is modified such that the effect of series remainder on estimation is reduced. The advantage of using first characteristic function and moments, over second characteristic function and cumulants, as fault detection statistics is also presented. It is shown that the proposed method can detect a class of faults that the former methods cannot; in particular faults with small amplitude ICs that have either different probability density function or identical probability density function of the ICs, but different low order moments of the ICs compared with the normal performance. Simulation results are used to show the effectiveness of the proposed method.


Cite This Article

M. Teimoortashloo and A. Khaki Sedigh, "A dynamic independent component analysis approach to fault detection with new statistics," Computer Systems Science and Engineering, vol. 33, no.1, pp. 5–20, 2018.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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