Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump

• Main Authors: Chuanqi Lu, Shaoping Wang
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sensors
• Subjects: aviation piston pump; degradation index; support vector regression; prognostics
• Online Access: https://www.mdpi.com/1424-8220/20/14/3854

Performance degradation prediction plays a key role in realizing aviation pump health management and condition-based maintenance. Thus, this paper proposes a new approach that combines a Gaussian mixture model (GMM) and optimized support vector regression (SVR) to predict aviation pumps’ degradation processes based on the pump outlet pressure signals. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not fully utilized, some useful IMF components are firstly chosen, and the corresponding multi-domain features are extracted from each selected component. Considering that it is not the case that all features are equally sensitive to degradation assessment, PCA is used to select more sensitive degradation features. Since the distribution of these extracted features is a stochastic process in feature space, meanwhile, self-information quantity can describe the uncertainty of system by measuring the average information quantity contained in the probability distribution, self-information quantity based on GMM is defined as degradation index (DI) to describe the degradation degree of the pump quantitatively. Finally, an SVR model is constructed to predict the degradation status of the pump. To achieve higher prediction accuracy, phase space reconstruction theory is first employed to determine the number of the inputs of the SVR model, then a new method combining particle swarm optimization (PSO) with grid search (GS) is developed to optimize the parameters of the SVR model. Finally, both the online data and historical data are utilized for the construction of the SVR model, respectively. The effectiveness of the proposed approach is validated by full life cycle data collected from an aviation pump test rig. The results demonstrate that the <i>DI</i> extracted from pump outlet pressure signals can effectively identify and track the current deterioration stage, and the established SVR model has better prediction ability when compared with previously published methods.