Pages 1-8
Cite this publication
Martínez, A., Sánchez, L., & Couso, I. (2013). Engine Health Monitoring for engine fleets using fuzzy radviz. 1-8. https://doi.org/10.1109/FUZZ-IEEE.2013.6622420
Abstract
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.
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
Details
- Date Published
-
July