A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Dangdang Du,Chaobo Hao,Xiaoliang Jia

doi:10.1155/2016/4615903

Abstract

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models.

Highlights

With increasing demands in the field of operation safety, asset availability, and economy, the health monitoring of aero engine has been widely considered as the key prerequisite for the competition of an airline company
radial basis kernel function (RBF)-chaos predicted data Single least squares support vector machine (LSSVM) predicted data the possible inherent biases of single LSSVM and makes full use of the advantages of individual member LSSVMs; (2) the phase space reconstruction (PSR) extracts the chaotic feature of the original data source and reconstructs data samples, which elucidates the input characteristic for the PSRPSO-(SK)LSSVM-(MK)LSSVM ensemble (PPLLE) model; (3) the particle swarm optimization (PSO) ensure that each individual LSSVM achieves the best performance; (4) the particular ensemble strategy of PPLLE employs an MKLSSVM and further enhances the prediction ability of the ensemble model
Designing a high accuracy and robust model for aero engine performance parameter (AEPP) prediction is quite challenging, since AEPP data is nonlinear, chaotic, and small-sample, and the traditional single prediction model may have some inherent biases. To solve this problem and to realize high prediction accuracy level, a new LSSVM ensemble model based on PSR and PSO is presented and applied to AEPP prediction in this paper

Summary

Introduction

With increasing demands in the field of operation safety, asset availability, and economy, the health monitoring of aero engine has been widely considered as the key prerequisite for the competition of an airline company. It is necessary to design a high accurate and robust prediction model for aero engine performance parameter (AEPP). The modeling accuracy of a single LSSVM is influenced by the input data source, and affected by its kernel function and regularization parameters [12]. A single LSSVM with optimal parameters and reconstructed input data samples may have an excellent prediction performance under certain circumstances, because its kernel function is fixed, it perhaps has some kinds of inherent bias under other cases. Ensemble model can make full use of diversity to compensate for disadvantages among the individual members, and the reasonable combination strategy is believed to be able to produce better prediction accuracy and generalization than single model [13,14,15,16].

An Overview of the Related Knowledge

Overall Process of Designing the PPLLE Model

Case Study

Conclusions