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So far, problems of intermittent fault (IF) detection and detectability havenot been fully investigated in the multivariate statistics framework. Thecharacteristics of IFs are small magnitudes and short durations, andconsequently traditional multivariate statistical methods using only a singleobservation are no longer effective. Thus in this paper, moving average T^2control charts (MA-TCCs) with multiple window lengths, which simultaneouslyemploy a bank of MA-TCCs with different window lengths, are proposed to addressthe IF detection problem. Methods to reduce false/missing alarms and infer theIFs' appearing and disappearing time instances are presented. In order toanalyze the detection capability for IFs, definitions of guaranteeddetectability are introduced, which is an extension and generalization of theoriginal fault detectability concept focused on permanent faults (PFs). Then,necessary and sufficient conditions are derived for the detectability of IFs,which may appear and disappear several times with different magnitudes anddurations. Based on these conditions, some optimal properties of two importantwindow lengths are further discussed. In this way, a theoretical framework forthe analysis of IFs' detectability is established as well as extendeddiscussions on how the theoretical results can be adapted to real-worldapplications. Finally, simulation studies on a numerical example and thecontinuous stirred tank reactor (CSTR) process are carried out to show theeffectiveness of the developed methods.

Detection and Detectability of Intermittent Faults Based on Moving Average T2 Control Charts with Multiple Window Lengths