The engine performance analysis, consisting of the determination of the engine state para-meters and their interpretation, represents a difficult task that is intuitively dealt with. In this research work a concept with a model-based part and an interpretation part for automated performance analysis of development engines has been developed. A synthesis model is the core of the model-based part. Whenever an engine operating point changes, it automatically regenerates a linear engine condition monitoring model that allows the engine state parameters to be determined from engine test data. Using a neuro-fuzzy system the interpretation part takes additional information from the engine development process into account. The neuro-fuzzy system also helps reduce the synthesis model iteration loops and avoid convergence problems. The functionality of the concept has been demonstrated in case studies based on helicopter development engine test data.
Performance analysis of helicopter development engine using a synthesis model and soft computing
2001
138 Seiten, 58 Bilder, 20 Tabellen, 70 Quellen
Theses
English
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