Prognostic Diagnosis of Ball Screw Preload Loss for Machine Tool through the Hilbert-Huang Transform and Multiscale Entropy Measure

Abstract

Detection of ball screw preload loss for machine tool has great significances in modern manufacturing processes for unexpected failures. This paper proposes a diagnostic method for ball screw preload loss through the Hilbert-Huang transform (HHT) and multiscale entropy(MSE) process. The method is used to diagnose a ball screw preload loss through the motor torque current signals when machine tool is in operation. Maximum dynamic preloads of 2%, 4%, and 6% ball screws were predesigned, manufactured, and conducted experimentally. Signal patterns are discussed and revealed by Empirical Mode Decomposition (EMD) with the Hilbert Spectrum. Different preload features are extracted and discriminated using HHT. The irregularity development of ball screw with preload loss is determined and abstracted via MSE based on complexity perception. The experiment results successfully show that the prognostic status of ball screw preload loss can be envisaged by the proposed methodology. The smart sensing for the health of the ball screw is available based on comparative evaluation of MSE by the signal processing and pattern matching of EMD/HHT. This diagnosis method realizes the purposes of prognostic effectiveness on knowing the preload loss and utilizing convenience.