Full Description
All real systems in nature - physical, biological and engineering ones - can malfunction and fail due to faults in their components. Logically,the chances for malfunctions increase with the systems' complexity. The complexity of engineering systems is permanently growing due to their growing size and the degree of automation, and accordingly increasing is the danger of fail ing and aggravating their impact for man and the environment. Therefore, in the design and operation of engineering systems, increased attention has to be paid to reliability, safety and fault tolerance. But it is obvious that, compared to the high standard of perfection that nature has achieved with its self-healing and self-repairing capabilities in complex biological organisms, fault management in engineering systems is far behind the standards of their technological achievements; it is still in its infancy, and tremendous work is left to be done. In technical control systems, defects may happen in sensors, actuators, components of the controlled object - the plant, or in the hardware or soft ware of the control framework. Such defects in the components may develop into a failure of the whole system. This effect can easily be amplified by the closed loop, but the closed loop may also hide an incipient fault from be ing observed until a situation has occurred in which the failing of the whole system has become unavoidable.
Contents
1. Introduction.- 2. Models in the diagnostics of processes.- 3. Process diagnostics methodology.- 4. Methods of signal analysis.- 5. Control theory methods in designing diagnostic systems.- 6. Optimal detection observers based on eigenstructure assignment.- 7. Robust H?-optimal synthesis of FDI systems.- 8. Evolutionary methods in designing diagnostic systems.- 9. Artificial neural networks in fault diagnosis.- 10. Parametric and neural network Wiener and Hammerstein models in fault detection and isolation.- 11. Application of fuzzy logic to diagnostics.- 12. Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems.- 13. Genetic algorithms in the multi-objective optimisation of fault detection observers.- 14. Pattern recognition approach to fault diagnostics.- 15. Expert systems in technical diagnostics.- 16. Selected methods of knowledge engineering in systems diagnosis.- 17. Methods of acqusition of diagnostic knowledge.- 18. State monitoring algorithms for complex dynamic systems.- 19. Diagnostics of industrial processes in decentralised structures.- 20. Detection and isolation of manoeuvres in adaptive tracking filtering based on multiple model switching.- 21. Detecting and locating leaks in transmission pipelines.- 22. Models in the diagnostics of processes.- 23. Diagnostic systems.
