Graphical Evolutionary Hybrid Neuro-Observer (GNeuroObs) System

The core project objective is to demonstrate the Graphical Evolutionary Hybrid Neuro-Observer (GNueroObs) System in conducting complex system workflow analysis for visualizing the interaction between components and subsystems. This software tool provides a state-of-the-art systems analysis framework compiling technologies involving: (a) advanced system and degradation modeling techniques using hybrid schemes with dynamic event network modeling; (b) novel automated knowledge generation; (c) high performance health monitoring (fault diagnosis) methodologies; and (d) advanced object-oriented software engineering practices. A novel methodology is developed and tailored to evaluate faults and engineering changes and their propagated effects within complex systems. This is made possible by understanding intrinsic natural behavior as well as degradation tendencies for entities (subsystems and components). The software toolset satisfies current OSD needs by including the capabilities of: (i) system decomposition; (ii) modeling of entity interrelations; (iii) graphical representation; and (iv) quantitative information overlay. The software is verified and validated with a representative target system. The overall expectation is that this software will enable engineers to obtain a more realistic understanding of complex system behavior.