Abstract:

A new prediction of bearing comprehensive rockmachine experiment platform based on phase space reconstruction and least squares support vector machine is presented. time series is embedded into high dimensional space, feature adaptively by using each phase point as the input of support vector machine. This method can effectively predict the of bearing the advantage of nonlinear regression of support vector machine. he predict result of the mentioned method better than that of BP neural network The method can be used in engineering practiceevaluat and predictthe state of bearings of shield machine effective guidancefor maintaining and repairing.

Key words: shield machine, comprehensive rockmachine experiment platform, phase space reconstruction, support vector machine, trendprediction