For any machine designed to generate power, or to fulfill its functions in general, maintenance actions will have an impact on many aspects of its overall capabilities, especially its performance and the length of its useful life. Since these are vital in order to generate maximum profit, the maintenance actions that affect them must be given serious consideration. For this reason, this research aims to propose a method that will enhance the cost saving potential with more accurately determined maintenance intervals and greater exploitation of the remaining life of the components by utilizing the capabilities of condition based monitoring.Initially, the research focuses on the description and the understanding of maintenance methods as they are performed within the aviation industry, but it also aims to investigate the state of the art Condition Based Monitoring Maintenance (CBMM) and its associated advantaged relating to the older methods. The thesis begins by describing the fundamental aviation maintenance management domains, paying particular attention to CBMM, and continues with the diagnostic and prognostic methods that are in use in order to support the condition monitoring concept. Next, a description is given of the actual implementations of the CBMM process, with the presentation of the maintenance enhancement systems, namely the Central Maintenance System and the Aircraft Condition Monitoring System.Lastly, a case study is presented of the estimation of the remaining useful life of a turbine blade, as it relates to the primary failure mode of creep. The case study endorses the use of the condition monitoring diagnostic methods discussed previously and also aims to demonstrate the predictive capabilities of the Engine Usage Diagnostics at both the design and the into-service stage. The created/simulated engine performance models concern several operating conditions of the engine while the impact of each of those on the remaining useful life of the blade is investigated.The benefit of this research is that it proposes a practical, effective, and relatively easy way to perform maintenance by predicting the need according to the usage. Additionally, the data required have already been measured, which paves the way for the creation of more intelligent engine control units. The contribution and innovation of the research is demonstrated by the fact that no similar approaches to creep life prediction have been published for the same type of engine, namely the CFM56 5B2. Last but not least, the results are presented in the most beneficial form of remaining hours before the failure.
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