|Title||Engine Health Monitoring for engine fleets using fuzzy radviz|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Martínez, A., Sánchez L., and Couso I.|
|Conference Name||2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)|
|Keywords||2D map, aerospace engineering, aerospace engines, aircraft, Bandwidth, condition monitoring, data visualisation, diagnostic tool, EHM data, engine fleets, engine health monitoring, Engines, fault diagnosis, fuzzy data, fuzzy Radviz, fuzzy set theory, genetic algorithms, genetic fuzzy system, Genetic Fuzzy Systems, Low Quality Data, Maintenance engineering, Market research, mechanical engineering computing, Monitoring, Radviz visualization algorithm, rule activation, single fuzzy state, states sequence, Temperature measurement, Turbines|
A new algorithm for assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. The diagnostic tool quantifies step changes, shifts and trends in EHM data by means of a transformation that aggregates concurrent readings of EHM data into a single fuzzy state. A Genetic Fuzzy System is used to detect the occurance of a specific trend of interest in the sequence of states. The activation of the rules is represented in a 2D map by means of an extension of the Radviz visualization algorithm to fuzzy data.