Here it deals with a class of systems that are becoming ubiquitous in the current and future "distributed world" made by countless "nodes",which can be cities, computers, people, etc., and interconnected by a dense web of transportation, communication, or social ties.The term "network", describing such a collection of nodes and links, nowadays has become commonplace thanks to our extensive reliance on "connections of interdependent systems" in our everyday life,for building complex technical systems, infrastructures and so on.
In an increasingly "smarter" planet, it is expected that such interconnected systems will be safe, reliable, available 24/7,and of low-cost maintenance.Therefore, health monitoring and fault diagnosis are of customary importance to ensure high levels of safety, performance, reliability, dependability, and availability. For example, in the case of industrial plants,faults and malfunctions can result in off-specification production,increased operating costs, production line shutdown,danger conditions for humans, detrimental environmental impact, and so on.Faults and malfunctions need to be detected promptly and their source and severity should be diagnosed so that corrective actions can be taken as soon as possible.
In the talk, an adaptive approximation-based distributed and networked fault diagnosis approach for large-scale nonlinear systems will be dealt with, by exploiting a "divide et impera" approach in which the overall diagnosis problem is decomposed into smaller sub-problems, which can be solved within “local” computation and communication architectures.The distributed detection, isolation and identification task is broken down and assigned to a network of "Local Diagnostic Units", each having a "local view" of he system. These local diagnostic units are allowed to communicate with each other through an information network to cooperate on the diagnosis of system components that may be shared or interconnected.