Fault diagnosis in hierarchical discrete-event systems

A framework for on-line passive fault diagnosis in hierarchical discrete-event systems (DES) is proposed. In this approach, the system model is broken into simpler substructures called D-holons. A state-based diagnoser is constructed for each D-holon. Fault diagnosis is accomplished using the state estimates provided by the D-holon diagnosers. At any given time, only a subset of the diagnosers are active, and as a result, instead of the entire model of the system, only the models of the D-holons associated with the active diagnosers are used. This reduces random access memory (RAM) requirements and thus, could be useful in complex multi-phase systems. Based on the D-holon model, the concept of phase-diagnosability is introduced to study failure diagnosability in cases where each component may be active only in some of the phases of operation. The computational complexity of constructing the transition systems required for diagnosis is exponential in the number of components. To reduce the computational complexity, we propose a semimodular approach with polynomial complexity for cases where interactions among system components are observable.