A Generalized Petri Net-based Diagnoser for Fault Detection and Isolation in Complex Processes

Recently, an efficient method for obtaining reduced interpreted Petri net, IPN, diagnoser was previously proposed for discrete event systems. This diagnoser consists of a single place and the same number of transitions that the system model has. Although, the current marking of this place (diagnoser) is enough to determine and locate faults occurring in a simple diagnosable discrete event system, it fails to determine and locate faults occurring in a complex non-diagnosable hydride systems that have discrete and continuous variables. Diagnosability means that the PN model is live, strongly-connected, and Transition-invariant (T-invariant); however, most of PN models are not diagnosable. This paper generalizes the IPN-based diagnoser to deals with such complex systems with ease. Chemical batch processes are employed to test the modified IPN-based diagnoser and the simulation results obtained indicate that the proposed diagnoser is promising for industrial processes.