Fault detection has a long tradition: the necessity to provide the most
accurate diagnosis possible for a process plant criticality is somehow
intrinsic in its functioning. Continuous monitoring is a possible way for early
detection. However, it is somehow fundamental to be able to actually simulate
failures. Reproducing the issues remotely allows to quantify in advance their
consequences, causing literally no real damage. Within this context, signed
directed graphs have played an essential role within the years, managing to
model with a relatively simple theory diverse elements of an industrial
network, as well as the logic relations between them.\\ In this work we present
a quantitative approach, employing directed graphs to the simulation and
automatic reconfiguration of a fault in a network. To model the typical
operation of industrial plants, we propose several additions with respect to
the standard graphs: 1. a quantitative measure to control the overall residual
capacity, 2. nodes of different categories - and then different behaviors - and
3. a fault propagation procedure based on the predecessors and the redundancy
of the system. The obtained graph is able to mimic the behaviour of the real
target plant when one or more faults occur. Additionally, we also implement a
generative approach capable to activate a particular category of nodes in order
to contain the issue propagation, equipping the network with the capability of
reconfigure itself and resulting then in a mathematical tool useful not only
for simulating and monitoring, but also to design and optimize complex plants.
The final asset of the system is provided in output with its complete
diagnostics, and a detailed description of the steps that have been carried out
to obtain the final realization
