TAILIEUCHUNG - Fault diagnosis with computational intelligence: Part 2

Continued from part 1, part 2 of the document Fault diagnosis with computational intelligence present the content: artificial neural networks in fault diagnosis, a gas Turbine scenario; two-stage neural networks based classifier system for fault diagnosis; soft computing models for fault diagnosis of conductive flow systems; fault diagnosis in a power generation plant using a neural fuzzy system with rule extraction; fuzzy neural networks applied to fault diagnosis; causal models for distributed fault diagnosis of complex systems. | 6. Artificial Neural Networks in Fault Diagnosis A Gas Turbine Scenario Stephen Ogaji and Riti Singh Gas turbines are used for aero and marine propulsion power generation and as mechanical drives for a wide range of industrial applications. Often they are affected by gas path faults which have hitherto been diagnosed by techniques such as fault matrixes fault trees and gas path analysis. In this chapter an artificial neural network approach to fault diagnosis is presented. The networks involved are trained to detect isolate and assess faults in some of the components of a single spool gas turbine. The hierarchical diagnostic methodology adopted involves a number of decentralised networks trained to handle specific tasks. All sets of networks were tested with data not used for the training process. The results when compared with available diagnostic tools show that significant benefits can be derived from the actual application of this technique. . Gas Turbine Faults Gas turbines GT are mechanical devices operating on a thermodynamic cycle with air as the working fluid. The air is compressed in a compressor mixed with fuel and burnt in a combustor with the gas expanded in a turbine to generate power used in driving the compressor and external loads thrust or shaftpower depending on requirements. The main gas path components of the GT which are compressor combustor and turbines are usually very reliable but could result in low availability of the whole unit if a forced unexpected outage is encountered as it can take some considerable time to repair them. This is made worse if the breakdown occurred when the maintenance crew was unprepared for it. Improving availability and reducing life cycle costs of the GT require maintenance schemes such as condition-based maintenance CBM which advocates maintenance only when it is necessary and at the appropriate time rather than after a fixed number of operating hours or cycles. For the operational health of the engine to be

• Cơ khí - Chế tạo máy
• Computational intelligence in fault diagnosis
• Fault diagnosis
• Artificial neural networks
• A gas Turbine scenario
• Soft computing models
• Fault diagnosis of conductive flow systems
• Computational intelligence methodologies
• Fault detection
• Industrial processes