TY - GEN
T1 - Using self-organizing maps for clustering and labelling aircraft engine data phases
AU - Faure, Cynthia
AU - Olteanu, Madalina
AU - Bardet, Jean Marc
AU - Lacaille, Jérôme
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/29
Y1 - 2017/8/29
N2 - Multiple signals are measured by sensors during a flight or a test bench and their analysis represent a big interest for engineers. These signals are actually multivariate time series created by the sensors present on the aircraft engines. Each of them can be decomposed into series of stabilized phases, well known by the experts, and transient phases. Transient phases are merely explored but they reveal a lot of information when the engine is running. The aim of our project is converting these time series into a succession of labels, designing transient and stabilized phases. This transformation of the data will allow to derive several perspectives: on one hand, tracking similar behaviours or patterns seen during a flight; on the other, discovering hidden structures. Labelling signals coming from the engines of the aircraft also helps in the detection of frequent or rare sequences during a flight. Statistical analysis and scoring are more convenient with this new representation. This manuscript proposes a methodology for automatically indexing all engine transient phases. First, the algorithm computes the start and the end points of each phase and builds a new database of transient patterns. Second, the transient patterns are clustered into a small 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.
AB - Multiple signals are measured by sensors during a flight or a test bench and their analysis represent a big interest for engineers. These signals are actually multivariate time series created by the sensors present on the aircraft engines. Each of them can be decomposed into series of stabilized phases, well known by the experts, and transient phases. Transient phases are merely explored but they reveal a lot of information when the engine is running. The aim of our project is converting these time series into a succession of labels, designing transient and stabilized phases. This transformation of the data will allow to derive several perspectives: on one hand, tracking similar behaviours or patterns seen during a flight; on the other, discovering hidden structures. Labelling signals coming from the engines of the aircraft also helps in the detection of frequent or rare sequences during a flight. Statistical analysis and scoring are more convenient with this new representation. This manuscript proposes a methodology for automatically indexing all engine transient phases. First, the algorithm computes the start and the end points of each phase and builds a new database of transient patterns. Second, the transient patterns are clustered into a small 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.
UR - https://www.scopus.com/pages/publications/85031298178
U2 - 10.1109/WSOM.2017.8020013
DO - 10.1109/WSOM.2017.8020013
M3 - Conference contribution
AN - SCOPUS:85031298178
T3 - 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings
BT - 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings
A2 - Lamirel, Jean-Charles
A2 - Olteanu, Madalina
A2 - Cottrell, Marie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017
Y2 - 28 June 2017 through 30 June 2017
ER -