Aero-Engine Condition Monitoring Based on Kalman Filter Theory

2012 ◽  
Vol 490-495 ◽  
pp. 176-181 ◽  
Author(s):  
You Gao ◽  
Nan Wang
Keyword(s):  
Kalman Filter ◽  
Condition Monitoring ◽  
Prediction Models ◽  
Engine Performance ◽  
Ar Model ◽  
Performance Parameters ◽  
Filter Theory ◽  
Aero Engine ◽  
Filter Approach ◽  
Engine Condition

The maintenance and management of civil aero-engine require advanced monitor schemes to evaluate aero-engine health and condition in order to ensure safety of aircraft and increase life of aero-engine. In this paper, we adopted Kalman filter approach to monitor an aero-engine health and condition by building prediction models of main aero-engine performance parameters (EGT, N1, N2 and WF). The AR model is introduced into the Kalman filter equations, which is a helpful technique to improve the accuracy of monitoring models of performance parameters. When the relative error goes beyond ±0.3%, alarms will be given. The prediction results show that Kalman filter theory using for AR regression prognostic is an effective approach in aero-engine monitoring.

Research of Aero-Engine Life Prediction Based on Take-Off EGTM

2011 ◽  
Vol 99-100 ◽  
pp. 286-292 ◽  
Author(s):  
Hong Bo Peng ◽  
Min Dan ◽  
Hong Chu Qu
Keyword(s):  
Condition Monitoring ◽  
Life Prediction ◽  
Great Influence ◽  
Engine Performance ◽  
Operating Costs ◽  
Estimation Methods ◽  
Aero Engine ◽  
Definition Of ◽  
Engine Condition ◽  
Engine Life

Life prediction is one important of area Engine research. Take-off EGTM is an important parameter to monitor Engine performance. Take-off EGTM have great influence on Engine life, Reducing EGT will help to extend Engine life on wing (LOW), thereby reducing operating costs. Aiming at aero-Engine condition monitoring, the definition of take-off EGT Margin is given, estimation methods and their application on Engine life prediction are discussed.

Study on a GUI Type Fault Diagnostic Program for a Turboshaft Engine Using Fuzzy and Neural Networks

2010 ◽  
Author(s):  
Changduk Kong ◽  
Youngju Koo ◽  
Seonghee Kho ◽  
Hyeok Ryu
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Real Time ◽  
Condition Monitoring ◽  
Hybrid Method ◽  
Performance Parameters ◽  
System A ◽  
Fault Diagnostic ◽  
Turboshaft Engine ◽  
Engine Condition

The helicopter to be operated in a severe flight environmental condition must have a very reliable propulsion system. On-line condition monitoring and fault detection of the engine can promote reliability and availability of the helicopter propulsion system. A hybrid health monitoring program using Fuzzy Logic and Neural Network Algorithms is can proposed. In this hybrid method, the Fuzzy Logic identify easily the faulted components from engine measuring parameter changes, and the Neural Networks can quantify accurately its identified faults. In order to use effectively the fault diagnostic system, a GUI (Graphical User Interface) type program is newly proposed. This program is composed of the real time monitoring part, the engine condition monitoring part and the fault diagnostic part. The real time monitoring part can display measuring parameters of the study turboshaft engine such as power turbine inlet temperature, exhaust gas temperature, fuel flow, torque and gas generator speed. The engine condition monitoring part can evaluate the engine condition through comparison between monitoring performance parameters the base performance parameters analyzed by the base performance analysis program using look-up tables. The fault diagnostic part can identify and quantify the single faults the multiple faults from the monitoring parameters using hybrid method.

scholarly journals Time-variant partial directed coherence for analysing connectivity: a methodological study

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110616 ◽  
Author(s):  
L. Leistritz ◽  
B. Pester ◽  
A. Doering ◽  
K. Schiecke ◽  
F. Babiloni ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Interaction Analysis ◽  
Ar Model ◽  
Coherence Analysis ◽  
Volume Conduction ◽  
Partial Directed Coherence ◽  
Methodological Study ◽  
Signal To Noise ◽  
The Past ◽  
Filter Approach

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular–respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Integration of 3D-CFD Component Simulation Into Overall Engine Performance Analysis for Engine Condition Monitoring Purposes

2018 ◽  
Author(s):  
C. Klein ◽  
F. Wolters ◽  
S. Reitenbach ◽  
D. Schönweitz
Keyword(s):  
Performance Analysis ◽  
Condition Monitoring ◽  
Guide Vane ◽  
Engine Performance ◽  
Operating Conditions ◽  
Tip Clearance ◽  
Inlet Guide Vane ◽  
Cfd Model ◽  
The Impact ◽  
Engine Condition

For an efficient detection of single or multiple component damages, the knowledge of their impact on the overall engine performance is crucial. This knowledge can be either built up on measurement data, which is hardly available to non-manufacturers or –maintenance companies, or simulative approaches such as high fidelity component simulation combined with an overall cycle analysis. Due to a high degree of complexity and computational effort, overall system simulations of jet engines are typically performed as 0-dimensional thermodynamic performance analysis, based on scaled generic component maps. The approach of multi-fidelity simulation, allows the replacement of single components within the thermodynamic cycle model by higher-order simulations. Hence, the component behavior becomes directly linked to the actual hardware state of the component model. Hereby the assessment of component deteriorations in an overall system context is enabled and the resulting impact on the overall system can be quantified. The purpose of this study is to demonstrate the capabilities of multi fidelity simulation in the context of engine condition monitoring. For this purpose, a 0D-performance model of the IAE-V2527 engine is combined with a CFD model of the appropriate fan component. The CFD model comprises the rotor as well as the outlet guide vane of the bypass and the inlet guide vane of the core section. As an exemplarily component deterioration, the fan blade tip clearance is increased in multiple steps and the impact on the overall engine performance is assessed for typical engine operating conditions. The harmonization between both simulation levels is achieved by means of an improved map scaling approach using an optimization strategy leading to practicable simulation times.

scholarly journals The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Chunxiao Zhang ◽  
Junjie Yue
Keyword(s):  
Kernel Function ◽  
Forecast Error ◽  
Engine Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Algorithm ◽  
Performance Parameters ◽  
Exhaust Temperature ◽  
Function Transformation ◽  
Aero Engine

The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT) time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS) method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

Research of Engine Performance Control Based on EGTM

2011 ◽  
Vol 148-149 ◽  
pp. 431-436
Author(s):  
Hong Bo Peng ◽  
Min Dan
Keyword(s):  
Condition Monitoring ◽  
Life Prediction ◽  
Great Influence ◽  
Engine Performance ◽  
Operating Costs ◽  
Estimation Methods ◽  
Performance Control ◽  
Definition Of ◽  
Engine Condition ◽  
Engine Life

Life prediction is one important of Engine research. Take-off EGTM is an important parameter to monitor Engine performance. Take-off EGTM have great influence on Engine life, Reducing EGT will help to extend Engine life on wing (LOW), thereby reducing operating costs. Aiming at Engine condition monitoring, the definition of take-off EGT Margin is given, estimation methods and their application on Engine life prediction are discussed.

scholarly journals Simultaneous Radar and Satellite Data Storm-Scale Assimilation Using an Ensemble Kalman Filter Approach for 24 May 2011

2015 ◽  
Vol 143 (1) ◽  
pp. 165-194 ◽  
Author(s):  
Thomas A. Jones ◽  
David Stensrud ◽  
Louis Wicker ◽  
Patrick Minnis ◽  
Rabindra Palikonda
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Radial Velocity ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
High Resolution Data ◽  
Core Convection ◽  
Filter Approach

Abstract Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

Thrust Measurement Model-Based Correction System for Turbine Engine Test Cell Dynamic Data

2015 ◽  
Author(s):  
Carl Palmer ◽  
Eric Hettler
Keyword(s):  
Kalman Filter ◽  
Engine Performance ◽  
Measurement Model ◽  
Test Cell ◽  
Performance Parameters ◽  
Turbine Engine ◽  
Engine Thrust ◽  
Performance Point ◽  
Turbojet Engine ◽  
Cell Data

This paper describes a turbine engine thrust estimator that computes “virtual measurements” of dynamic engine thrust and other parameters of interest from test cell data in a very short amount of time. The system ‘tunes’ a user’s engine model, as developed in the commonly used Numerical Propulsion System Simulation (NPSS), by optimizing system biases and health parameters to match the sensor outputs of a set of steady state data points across the operating range. The tuned model is then used to create a constant gain extended Kalman filter that is added directly within the NPSS model code. The results, including thrust, from this NPSS model with Kalman filter are then presented as the ‘actual’ corrected data. Key aspects of the system include: • Utilization of and tight integration with NPSS. This ensures that the results always preserve mass and energy, and are realistic from an engine performance point of view. • Flexibility; any NPSS model for any gas turbine engine can be used. • The ability for the whole tuning process to ‘correct’ not just noisy test data, but also performance parameters within the user’s actual NPSS model. • A GUI that leads the user through each step of the process, such as matching NPSS variable names to signals in the actual test cell data files, and selection of ‘tuners’ (performance parameters, sensor errors) and Kalman filter variables. The system has been tested both on a simulated two spool turbojet engine, plus an actual two spool turbojet engine, with recorded test cell data.

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