Abstract: To deal with the difficulty of selecting an appropriate embedding dimension for aeroengine condition time series prediction, a method based on least squares support vector machine (LSSVM) with adaptive embedding dimension is proposed. In the method, the embedding dimension is identified as a parameter that affects the accuracy of the aeroengine condition time series prediction; particle swarm optimization (PSO) is applied to optimize the hyperparameters and embedding dimension of the LSSVM prediction model; cross-validation is applied to evaluate the performance of the LSSVM prediction model; and matrix transform is applied to the LSSVM prediction model training to accelerate the cross-validation evaluation process. Experiments on an aeroengine exhaust gas temperature (EGT) prediction demonstrates that the method is highly effective in embedding dimension selection. In comparison with conventional aeroengine condition time series prediction methods, the LSSVM prediction model with the optimized hyperparameters and embedding dimension has better prediction performance.

Key words: least squares support vector machine, particle swarm optimization, cross-validation, aeroengine, condition time series prediction