@Article{csse.2018.33.005,
AUTHOR = {M. Teimoortashloo, A. Khaki Sedigh},
TITLE = {A Dynamic Independent Component Analysis Approach To Fault Detection With New Statistics},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {33},
YEAR = {2018},
NUMBER = {1},
PAGES = {5--20},
URL = {http://www.techscience.com/csse/v33n1/39953},
ISSN = {},
ABSTRACT = {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.},
DOI = {10.32604/csse.2018.33.005}
}