Faults/failures in technical systems may have many undesired consequences as damage to technical parts of plants, endangering of human life or pollution of the environment. Equipment failures may also have profound negative impact on production costs and product quality. The development of fault diagnosis and reliability design methods allowing early detection of faults/failures is crucial in order to protect complex manufacturing machineries, to increase human life safety and to support decision making on emergency actions and repairs. Moreover, in highly automated industrial systems where maintenance or repair cannot be carried-out immediately, it is crucial to employ reliable fault-tolerant control systems capable of ensuring acceptable performance even in the presence of faults. As most industrial plants are inherently nonlinear and the faults may often amplify the nonlinearities by driving the plants from a relatively linear operating point into a more nonlinear operating region, the study of fault-tolerant control and reliability design for nonlinear systems has always been a very active research topic for both theoretical and practical reasons. In the attempt to solve the fault-tolerant control and reliability design problem for systems with significant nonlinearities and a wide operating range, methods such as neural networks, fuzzy systems, and evolutionary algorithms have been receiving considerable attention due to their capabilities of forming arbitrarily accurate approximation to any continuous nonlinear functions. Therefore, it is quite important to study the novel nonlinear fault-diagnosis methods and present the corresponding theoretical analysis to guarantee the stability and performance of the whole fault tolerant system.
This task force will bring together academics, engineers and practitioners active in the fields of fault diagnosis, intelligent fault tolerant control, reliability analysis, intelligent reliability design, and their application in process monitoring and maintenance.