Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

ESTIMATING MULTI-COUNTRY PROSPERITY INDEX: A TWO-DIMENSIONAL SINGULAR SPECTRUM ANALYSIS APPROACH

With the development of the global economy, interaction among different economic entities from one region is intensifying, which makes it significant to consider such interaction when constructing composite index for each country from one region. Recent advances in signal extraction and time series analysis have made such consideration feasible and practical. Singular spectrum analysis （SSA） is a well-developed technique for time series analysis and proven to be a powerful tool for signal extraction. The present study aims to introduce the usage of the SSA technique for multi-country business cycle analysis. The multivariate SSA （MSSA） is employed to construct a model-based composite index and the two dimensional SSA （2D-SSA） is employed to establish the multi-country composite index. Empirical results performed on Chinese economy demonstrate the accuracy and stability of MSSA-based composite index, and the 2D-SSA based composite indices for Asian countries confirm the efficiency of the technique in capturing the interaction among different countries.