On Data Driven Multiple Fault Diagnosis Method
Chenglin Wen · Control Engineering of China · 2008
Designated component analysis(DCA) which can combine statistic pattern building and physics pattern building can effectively avoid pattern composing problem so as to make fault diagnosis.For orthogonal variation pattern must be designed in advance,a progressive DCA multi-fault diagnosis method is given based on the idea of pattern grouping.Common fault patterns are grouped into orthogonal subgroups,then according to each designated pattern,DCA is implemented on the observation data or the residual.And significance of each fault pattern is computed to identify whether the corresponding fault will occur.Simulation result involving observation datum of 6 coexistent faults show that the DCA multifault diagnosis algorithm can effectively make multi-fault diagnosis without explanation of physical meaning of the diagnosing result.