Shaft alignment prediction is essential for the development of effective coupling and rotating equipment maintenance systems. In this paper, we present a modified support vector regression (SVR) approach for shaft alignment predictions based on fast Fourier transform generated spectra data. The modified SVR approach uses data-dependent parameters in order to reduce computation time and achieve better predictions. The spectra data used is characterized by a large number of descriptors and very few data points. The strengths of SVR for shaft misalignment prediction include its ability to represent data in high-dimensional space through kernel functions. We reduce the dimension of the data using a multivariate AIC criterion in order to guarantee that the selected spectra are response dependent. We compare the performance of SVR with two of the most popular techniques used in condition monitoring, partial least squares, and principal components regression. Our results show that we can improve the performance of shaft misalignment prediction using SVR and the approach compares very favorably with partial least squares and principal components regression approaches. Also, we present a quantitative measure, shaft misalignment monitoring index, which can be used to facilitate easy identification of the alignment condition and as input to maintenance systems design.
Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression