Gas turbine engine gas path anomaly detection using deep learning with Gaussian distribution

2017 ◽  
Author(s):  
Hui Luo ◽  
Shisheng Zhong
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Gas Turbine ◽  
Gaussian Distribution ◽  
Gas Turbine Engine ◽  
Turbine Engine

Life Cycle Performance Estimation and In-Flight Health Monitoring for Gas Turbine Engine

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Feng Lu ◽  
Wenhua Zheng ◽  
Jinquan Huang ◽  
Min Feng
Keyword(s):  
Life Cycle ◽  
Anomaly Detection ◽  
Gas Turbine ◽  
Rapid Prototype ◽  
Gas Turbine Engine ◽  
Performance Estimation ◽  
Prototype System ◽  
Turbine Engine ◽  
Abnormal Detection ◽  
Rapid Prototype System

A long-term gas-path fault diagnosis and its rapid prototype system are presented for on-line monitoring of a gas turbine engine. Toward this end, a nonlinear hybrid model-based performance estimation and abnormal detection method are proposed in this paper. An adaptive extended Kalman particle filter (AEKPF) estimator is developed and used to real time estimate engine health parameters, which depict gas turbine performance degradation condition. The health parameter estimators are then pushed into a buffer memory and for periodical renewing baseline model (BM) performance, and the BM is utilized to detect engine anomaly over its life course. The threshold in abnormal detection schemes is adapted to the modeling errors during the engine lifetime. The rapid prototyping system is designed and built up based on the National Instrument (NI) CompactRIO (CRIO) for evaluating gas turbine engine performance estimation and anomaly detection. A number of experiments are carried out to demonstrate the advantages of the proposed abnormal detection scheme and effectiveness of the designed rapid prototype system to the problem of gas turbine life cycle anomaly detection.

Advanced Diagnostic and Prognostic Technologies for Gas Turbine Engine Risk Assessment

2000 ◽  
Author(s):  
Michael J. Roemer ◽  
Gregory J. Kacprzynski
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Management Practices ◽  
Probabilistic Models ◽  
Gas Turbine Engine ◽  
Probabilistic Methods ◽  
Great Opportunity ◽  
Turbine Engine

Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices. This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total ‘health’ of an engine, both mechanically and thermodynamically. This includes the ability to account for uncertainties from engine transient conditions, random measurement fluctuations and modeling errors associated with model-based diagnostic and prognostic procedures. The implementation of probabilistic methods in the diagnostic and prognostic methodology is critical to accommodating for these types of uncertainties.

Flexible manufacturing in repair of gas turbine engine components

1992 ◽  
Author(s):  
KIRK D ◽  
ANDREW VAVRECK ◽  
ERIC LITTLE ◽  
LESLIE JOHNSON ◽  
BRETT SAYLOR
Keyword(s):  
Gas Turbine ◽  
Flexible Manufacturing ◽  
Gas Turbine Engine ◽  
Turbine Engine ◽  
Engine Components

Fracture of a Compressor Stator Blade in a Gas Turbine Engine

2013 ◽  
Vol 50 (1) ◽  
pp. 43-49
Author(s):  
A. Neidel ◽  
B. Matijasevic-Lux
Keyword(s):  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Turbine Engine ◽  
Stator Blade
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