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Anomaly detection for health management of aircraft gas turbine engines
Devendra Tolani
, Murat Yasar
, Shin Chin
,
Asok Ray
Mechanical Engineering
Research output
:
Contribution to journal
›
Conference article
›
peer-review
40
Link opens in a new tab
Scopus citations
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Keyphrases
Aircraft Gas Turbine Engines
100%
Anomaly Detection
100%
Health Management
100%
Time Series Data
66%
Slow Time
66%
Early Detection
33%
Health Monitoring
33%
Principal Coordinate Analysis (PCoA)
33%
Gas Turbine Engine
33%
Artificial Neural Network
33%
Simulation Model
33%
Traditional Pattern
33%
Pattern Search
33%
Pattern Recognition
33%
Recognition Tool
33%
Information Theory
33%
Symbolic Dynamics
33%
Scale Anomaly
33%
Fasting Time
33%
Dynamic Theory
33%
Engine Simulation
33%
Engineering
Gas Turbine Engine
100%
Aircraft Gas Turbine
100%
Anomaly Detection
100%
Data Series
66%
Pattern Recognition
66%
Slow Time Scale
66%
Health Monitoring
33%
Early Detection
33%
Principal Components
33%
Component Analysis
33%
Simulation Model
33%
Fast Time Scale
33%
Artificial Neural Network
33%
Computer Science
Anomaly Detection
100%
Time Series Data
100%
Pattern Recognition
100%
Recognition Algorithm
50%
Component Analysis
50%
Principal Components
50%
Information Theory
50%
Simulation Model
50%
Early Detection
50%
Anomaly-Based Detection
50%
Artificial Neural Network
50%
Medicine and Dentistry
Pattern Recognition
100%
Time Series Analysis
100%
Health Care Management
100%
Principal Component Analysis
50%
Earth and Planetary Sciences
Artificial Neural Network
33%
Physics
Artificial Neural Network
33%