Anomaly Detection in Aircraft Gas Turbine Engines

Anomaly Detection in Aircraft Gas Turbine Engines

Symbolic identification for fault detection in aircraft gas turbine engines

This article presents a robust and computationally inexpensive technique of component-level fault detection in aircraft gas-turbine engines. The underlying algorithm is based on a recently developed statistical pattern recognition tool, symbolic dynamic filtering (SDF), that is built upon symbolization of sensor time series data. Fault detection involves abstraction of a language-theoretic desc...

Aircraft Gas Turbine Engines Technical Condition Identification System

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the usin...

Multi-sensor information fusion for fault detection in aircraft gas turbine engines

The article addresses data-driven fault detection in commercial aircraft gas turbine engines in the framework of multisensor information fusion and symbolic dynamic filtering. The hierarchical decision and control structure, adopted in this article, involves construction of composite patterns, namely, atomic patterns extracted from single sensors, and relational patterns representing cross-depe...

Detection of Incipient Bearing Faults in Gas Turbine Engines

Development of robust and highly sensitive algorithms for detecting incipient bearing faults in gas turbine engines will greatly benefit both military and civil aviation through improved aircraft reliability and maintainability. Techniques including advanced vibration analysis and oil debris monitoring have proven effective in laboratory and industrial settings, but factors including poor trans...