Robust codiagnosability of discrete-event systems against permanent loss of observations

Different notions of robust diagnosability of discrete-event systems (DESs) have been introduced in the literature. In all these works, the objective is the detection of unobservable fault events in DESs subject to uncertainties in the observation of the events and/or in the plant model. Recently, the so-called robust diagnosability of DESs against permanent loss of observations (RDPLO) has been introduced, where the uncertainty is in the observable event set of the system. In this regard, the language generated by the system is said to be robustly diagnosable if it is possible to detect the fault occurrence, within a bounded delay, even when some sensors permanently fail to communicate the occurrence of the events to the diagnoser. In this paper, we extend the definition of RDPLO to the decentralized case leading to the definition of robust codiagnosability against permanent loss of observations (RCPLO). The paper also addresses the issue of online implementation, and we propose an efficient scheme to carry out online robust codiagnosis against permanent loss of observations. Another contribution of the paper is the development of a polynomial time algorithm for the verification of the RCPLO.