Prognostic diagnosis of the preload loss for single nut ball screw through vibration signals

This paper proposes method of diagnosing ball screw preload loss through Ihe Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process. The proposed method can diagnose ball screw preload loss through vibration signals when the machine tool is in operation. Maximum dynamic preload of 2%, 4%, and 6% ball screws were predesigned, manufactured, and tested experimentally. Signal patterns are acquisitioned and discussed using Empirical Mode Decomposition(EMD)with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of a ball screw with preload loss is determined and abstracted using MSE based on complexity perception. Experiment results show that the proposed method can predict the status of ball screw preload loss. Smart sensing for the prognostic health of the ball screw is determined by a comparative evaluation of MSE when the signal pattern matching through the EMD and HHT are available. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss of ball screws and utilizing convenience for industrial applications.