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This article deals with the fault detection problem for a class of discrete-time networked systems with multiple state delays and unknown input. Two kinds of incomplete measurements, namely measurements with random communication delays and measurements with stochastic packet losses, are simultaneously considered. By properly augmenting the states of the original system and the fault detection filter, the fault detection filter design problem can be formulated as an H∞ filtering problem. Attention is focused on the design of a fault detection filter such that, for all unknown input and incomplete measurements, the error between residual and weighted fault is made as small as possible. A sufficient condition for the existence of the desired fault detection filter is established in terms of a linear matrix inequality. A numerical example is provided to illustrate the effectiveness and applicability of the proposed techniques. [ABSTRACT FROM AUTHOR]

Intermittent faults (IFs) have properties such as intermittency, random magnitude and random duration time. Hence the detection of IFs means: (i) to detect not only all the appearing time but also all the disappearing time of IFs and (ii) to detect the appearing time of an IF before this IF disappears, and the disappearing time of an IF before the subsequent IF appears. Within a statistical framework, the detection of scalar IFs in continuous linear stochastic dynamic systems has been mainly studied. Based on the sliding window, an analytical residual is generated, and two hypothesis tests are implemented to detect the appearing and disappearing times of IFs. In addition, a necessary and sufficient condition for the detectability of IFs is obtained, and the detection speed can be fast enough. Theoretical analysis and numerical simulations fully verify that IFs can be successfully detected. [ABSTRACT FROM AUTHOR]