Design Aircraft Engine Bivariate Data Phases using Change-Point Detection Method and Self-Organizing Maps

Analysing multivariate time series created by sensors during a flight or a bench test represents a new challenge for aircraft engineers. Each time series can be decomposed univariately into a series of stabilised phases, well known by the expert, and transient phases that are merely explored but very informative when the engine is running. Our project aims at converting these time series into a succession of labels, designing transient and stabilised phases in a bivariate context. This transformation of the data will allow several perspectives: tracking similar behaviours or bivariate patterns seen during a flight, detecting frequent or rare sequences of labels during a flight and, discovering hidden multivariate structures. This manuscript proposes a methodology to automatically cluster all engine transient phases. First, the algorithm builds a new database of transient patterns with a change-point detection method. Second, the bivariate transient patterns are clustered into a ranked number of typologies, which will provide the labels. The clustering is implemented with Self-Organizing Maps [SOM]. All algorithms are applied on real flight measurements with a validation of the results from expert knowledge.