Aero engine performance deterioration highly influences its reliability, availability and life cycle. Predictive maintenance is therefore a key figure within Industry 4.0, which guarantees high availability and reduced downtime thus reduced operational costs for both military and civil engines. This leads to maintenance on demand and needs an effective engine health monitoring system. This paper overviews the work carried out on aero engine health monitoring, diagnostic and prognostic techniques based on gas path performance parameters. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality data base using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Generating dependable information about the health condition of the engine is therefore a requisite for a successful implementation of condition-based maintenance. Based on this study, further research can be attempted to predict residual life of critical components using degradation pattern from aero engine performance data bank which will be an invaluable asset for engine designers as well as for operators.
Aero engine performance analysis is very important for engines under development as well as for engines in service for condition monitoring. Predictions of aero engine performance and ability of building the simulation model is an invaluable asset for designers, manufacturer and end-use operator. This paper presents the methodology in establishing the baseline performance of a twin spool mixed flow low bypass turbofan engine through extensive testing at engine test bench. The baseline data is used to validate a GasTurb model which is subsequently used for assessment of off-design performance and component degradation responsible for performance deterioration at various service hours. The estimated exhaust gas temperatures by the model for degraded engines are in good agreement with the measured data. The model further assesses the drop in HP compressor efficiency and shift in operating line which will be very useful for taking judicious decision for withdrawal of engines and is expected to reduce or delay withdrawals and increase the availability of engines at operating base.
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