Abstract
In order to predict aeroengine wear accurately and automatically, as a predictor, support vector regression (SVR) was optimized by means of particle swarm optimization (PSO). The guided mutation strategy of PSO (GMPSO) is presented herein to determine the proper structure parameters of an SVR, as well as the embedding dimensions of the training samples. The guided mutation strategy was able to increase the diversity of particles and improve the probability of finding the global extremum. Furthermore, single-step and multi-step prediction methods were designed to meet different accuracy requirements. A prediction comparison study on spectral analysis data was carried out, and the contrast experiments show that compared with SVR optimized by means of a traditional PSO, a neural network and an auto regressive moving average (ARMA) prediction model, the SVR optimized by means of the GMPSO approach produced prediction results not only with higher accuracy, but also with better consistency.
Highlights
Our research demonstrates that the factors affecting the prediction accuracy of the support vector regression (SVR) include γ, C, and ε
The principle of k-fold cross validation (k-CV) is that the original data are divided into k groups, each group is taken as the test data successively, and the rest of the groups are taken as the training data, so there are k test results, and the mean of k results will be the output of the fitness function
In order to verify the accuracy and consistency of the GMSO-SVR prediction method proposed in this paper, it was compared with the commonly used back propagation (BP)
Summary
The aeroengine is the crucial component for guaranteeing flight safety. The bearings and gears, as wearing parts, are widely used in the accessory transmission system, and they cause wear faults in the harsh work environment, threatening flight safety. By analyzing and predicting the composition and concentration of abrasives, the wear status and the internal fault mechanism of the aeroengine can judged effectively. A novel deep learning-based method was introduced to monitor wear status, which improved the ability to undertake wear data processing [6]. Support vector regression (SVR) has been proposed as a method of predicting the wear status. Particle swarm optimization (PSO) has been used to optimize the structure parameters of SVRs and the embedding dimension of training samples, which can ensure the accuracy and consistency of the prediction results.
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