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.