A graph-based framework for complex system simulating and diagnosis with automatic reconfiguration

Abstract

In this work we present a novel approach for modeling complex industrial plants, employing directed graphs to the simulation and automatic reconfiguration after failures. The framework offers the possibility to model the failure propagation, estimating the overall condition of the system before and after the damage and exploit such a health index for dynamic recalibration. To model the typical operation of industrial plants, we propose several additions with respect to the standard graphs: i) a quantitative measure to control the overall condition of the system ii) nodes of different categories–and then different behaviors–and iii) a fault propagation procedure based on the predecessors and the redundancy of the system. The obtained graph is able to mimic the behavior of the real target plant when one or more faults occur. Additionally, we also implement a generative approach capable of activating a particular category of nodes in order to contain the issue propagation, equipping the network with the capability of reconfiguring itself and resulting 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 the output with its complete diagnostics and a detailed description of the steps that have been carried out to obtain the final realization.

Keywords

• risk-analysis
• graph-theory
• fault-diagnosis
• automatic reconfiguration
• industrial applications
• signed-directed-graphs