Civilaircraft Engine Health Condition Prediction Based On Intelligent Learning Models

The health condition prediction of civil aircraft engine is the basis for makingreasonable scheduling and maintenance plans, and is the support technology forensuring operation safety and improving economy. As data-driven models withoutprior hypotheses, intelligent learning models represented by neural networks can betrained using the health condition data directly, which overcomes the difficulties ofestablishing exact mechanism models for engine health condition prediction.However, the complex nonlinear time-varying characteristics of the aircraft enginehealth degradation process bring about difficulties to the actual use of intelligentlearning models.In view of such problems and the requirements of civil aircraft engine healthcondition prediction, the adaptive noise reducing method, the single global modelingmethod, the ensemble local modeling method and the uncertainty assessing methodfor prediction results are studied in this paper.An engine health signal noise reducing method based on singular valuedecomposition (SVD) enhanced by empirical mode decomposition (EMD) isproposed. The trend component is abstracted from the original health signal usingEMD, and then the residual is de-noised using SVD. Since the disturbance of thetrend component is eliminated, the singular values for signal reconstruction can beselected adaptively according to the singlular value spectrum. The given method isvalidated through the noise reducing of the actual engine health signals.In view of the problem that the process neural network (PNN) with continuousfunction inputs can not be trained using discrete samples directly, a discrete inputprocess neural network model (DPNN) is presented. DPNN takes discrete vectors asinputs and utlize convolution sum to realize the time aggregate operation. Thus,DPNN can avoid problems such as parameter selection and information loss duringprocedures of function fitting and expanding required by PNN with continuousfunction inputs. The engine health condition prediction results show that, DPNNperforms comparable to PNN with continuous function inputs while with betteroperability.To overcome the difficulties of model optimization required by single globalmodels for engine health condition prediction, two ensemble prediction models with static and dynamic combining weights respectively based on the improvedAdaBoost.RT are proposed. The error function of AdaBoost.RT is improved, and anadaptive adjustment strategy is adopted to adjust the threshold during the trainingprocess. Then, DPNN, ANN and the extreme learning machine are utilized as weaklearners to construct ensemble models for engine health condition prediction. Forensemble models with dynamic combining weights, the weak learners are evaluatedusing the neighboring samples during the training process, and the dynamiccombining weights of the weak learners are generated according to their performanceon the neighboring samples of the testing sample in the training sample set. Since thelocal performance of the weak learners can be well mined, the ensemble models withdynamic combining weights are superior to their counterparts with static combiningweights. The prediction results of engine health conditions show that the ensemblemodels always perform better than single global models. And the performancerequirements for predictors can be reduced while being utilized as weak leaners.According to the fact that the point prediction results are accompanied withuncertainty, the Bootstrap based prediction interval estimation method is employed toestimate the prediction intervals of the presented models, thus to achieve quantitativeassessment of the reliability and accuracy of the prediction results.Based on such reseach, the “system for engine removal date prediction” isdeveloped according to the actual requirements of Air China. As a key subsystem ofthe “aircraft engine health management and maintenance decision support system”,the developed system acquires health condition information from its parent system toexecute engine removal date prediction, and the results are provided to themaintenance planning module for maintenance plan making, which implementsseamless integration from engine condition monitoring to maintenance decisionmaking.