Civil Aeroengine GAS Path Parameter Deviation Mining Method With Application

Aero-engine is the heart of aircraft, its health condition has a great impact onflight security. The gas path parameter deviation solution and performance statusevaluation method established are foundation of engine condition monitoring, andthey are also urgent needs of various airlines. Based on the analysis of domestic andinternational aero-engine performance monitoring research, according to the need ofairline, first the message parsing technology and gas path parameter independentvariables selection method are studied in this paper. Gas path parameter deviationmining method based on engine baseline and deviation regression solution based onparameter core transform SVM are proposed. Then the gas path analysis technologybased on the deviation is studied, mainly the deviation forecasting, deviationsmoothing, engine performance evaluation based on high dimension deviation andgas path fault diagnosis based on deviation are studied. Finally, the theoreticalmethods and calculation results are applied to the development of aero-engineperformance monitoring system.The relevant parameters must be selected before the aero-engine gas pathparameter deviation solving, so first the selected method of argument parameterssets relevant to deviation is studied in this paper. In order to eliminate the hazards toregression analysis of multiple correlation and repeat property, the variance inflationfactor method is used to diagnose multiple correlations between argumentparameters, the knowledge of aero-engine principle is used to analyze the non-linearrelationship between gas path parameter independent variables. According to thecomplex non-linear relationship between the argument parameters, the engineargument parameters selection method based on the combination of mean impactvalue(MIV) and wavelet neural network is proposed, which can eliminate unrelatedand tiny related independent variable effect to regression, and which reasonablyrealize the dimension reduction purpose for regression theory of this article.Then the deviation mining method is proposed. The deviation model is dividedinto difference of parameter standardization model and performance baseline modelaccording the meaning of gas path parameter deviation, and the gas path parameterstandardization model is derived. Taking into account the influence of ambienttemperature and humidity, and sensor offset, the corrected uncertain exponentstandardized model based on deviation is proposed. Uncertain coefficientspolynomial baseline model is used according to function approximation theory. Theuncertain coefficients gas path parameter deviation model is obtained using theabove two models to subtract each other, the regression analysis is done by inputting the original manufacturer historical data into the deviation model. The deviationmodel is mined accurately by using the improved Gauss-Newton iterative method tosolve the deviation model, and the deviation is accurately self-solved using thatdeviation model.The solution method of aero-engine gas path parameter deviation under smallnumber engine in the fleet is studied in this paper. Gas path parameter deviationsolution method based on aero-engine state parameter core transform support vectorregression machine is proposed, the input set local dimensionality reduction isachieved by increasing the optimization parameters according to the similarproportional relationship between the engine measurement parameters, thecomplexity of the model is reduced and calculation efficiency is improved. Thetraining set constructor based on Euclidean distance arrangement rule ofhigh-dimension space point to origin point is proposed in order to improve thegeneralization ability of support vector machines. The algorithm can improve thegas path deviation solution accuracy and solution speed of that model.Gas path parameter deviation forecasting techniques is studied on the basis ofthe deviation solving. A fractional nonlinear polymerization process neural networkprediction model based on discrete Walsh transform is proposed according to truemutation data exist in gas path parameter deviation time series, since the rationalfunction has better nonlinear approximation ability, nonlinear rational fractionalspace aggregation operations substitute for linear aggregation operations. In order toavoid the loss accuracy of the discrete data in fitting process, the network learningmethod is proposed based on Levenberg-Marquardt(LM) algorithm of discreteWalsh transform, the inner product of measurement data discrete Walsh transform isused to substitute the integral operator in process neural network, that simplify thecalculation process, improve the calculation speed. The conclusion can drawn frominstance that the forecasting model and network learning algorithm proposed in thispaper can improve prediction accuracy and prediction sensitivity on mutations dataand has better nonlinear approximate ability.In the gas path parameter smoothing technology, the outlier analysis andsimilar exponential smoothing combination of gas path parameter deviation smoothprocessing technique is proposed in the paper, and the parameters are optimizedusing sample mean square error minimum as objective function, the algorithm canreasonably smooth the deviation series and reserve the outlier data. In order toovercome the one-sidedness using one dimension gas path parameter deviation toevaluate engine performance, the engine performance evaluation model based ondiscrete Hopfield network is proposed, the high dimension parameter which cancharacterize engine performance is used to evaluate engine performance, whichmake the evaluation results has better scientific. In order to overcome difficult to distinguish similar fault, low efficiency of fault fingerprints figure used and requiresstrong expertise difficulties, the gas path fault diagnosis based on Self-OrganizingFeature Map(SOFM) under fingerprints figure is proposed in this paper, the methodcan improve the efficiency and accuracy of fault diagnosis, when new fault modelsis adding, it can quickly and easily achieve fault re-classification, to achieveaccurate and fast fault diagnosis.Finally, the key technology of performance baseline and gas path parameterdeviation in performance monitoring applications are studied based on engineproject management application of air china, and from the actual needs of thecompany’s engineering department, a Web-based aero-engine performancemonitoring system is developed, which can achieve self-solving techniques of thegas path parameter deviation, and provide data support to other modules ofWeb-based aero-engine management and maintenance decision support system,achieve auto alert and trend analysis of state parameter and engine health statusqueue function.