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.

Steady-State Modeling of Gas Turbine Engines Using the Numerical Propulsion System Simulation Code

2010 ◽  
Author(s):  
Scott M. Jones
Keyword(s):  
Steady State ◽  
Gas Turbine ◽  
System Simulation ◽  
Gas Turbine Engine ◽  
Propulsion System ◽  
Performance Estimation ◽  
Basic Knowledge ◽  
Turbine Engine ◽  
Starting Point ◽  
Engine Cycle

The Numerical Propulsion System Simulation (NPSS) code was created through a joint United States industry and National Aeronautics and Space Administration (NASA) effort to develop a state-of-the-art aircraft engine cycle analysis simulation tool. Written in the computer language C++, NPSS is an object-oriented framework allowing the gas turbine engine analyst considerable flexibility in cycle conceptual design and performance estimation. Furthermore, the tool was written with the assumption that most users would desire to easily add their own unique objects and calculations without the burden of modifying the source code. The purpose of this paper is twofold: first, to present an introduction to the discipline of thermodynamic cycle analysis to those who may have some basic knowledge in the individual areas of fluid flow, gas dynamics, thermodynamics, and turbomachinery theory but not necessarily how they are collectively used in engine cycle analysis. Second, this paper will show examples of performance modeling of gas turbine engine cycles specifically using Numerical Propulsion System Simulation concepts and model syntax. Current practices in industry and academia will also be discussed. While NPSS allows both steady-state and transient simulations and is written to facilitate higher orders of analysis fidelity, the pedagogical example will focus primarily on steady-state analysis of an aircraft mixed flow turbofan at the 0-D and 1-D level. Ultimately it is hoped that this paper will provide a starting point by which both the novice cycle analyst and the experienced engineer looking to transition to a superior tool can use NPSS to analyze any kind of practical gas turbine engine cycle in detail.

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.

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