Laplacian Eigenmaps and Mahalanobis Distance based Health Assessment Methodology for Ball Screw

doi:10.3901/JME.2017.15.125

Abstract

Abstract: The performance of ball screw is one of the important factors which affect the CNC machining precision. A method to assess the performance degradation and health status of ball screws is proposed based on the combination of Laplacian eigenmaps and Mahalanobis distance analysis to establish a nonlinear mapping relationship between the characteristics of the signals and health status of the ball screw. The health values are calculated to represent the degree of the performance degradation. Experiments have been conducted by testing the ball screws of different health status. The proposed method is performed on the speed and torque signals from the drive motor for validation. Compared with the results from the traditional dimensionality reduction methods, the results show that the proposed model is more accurate and robust. The model is featured with both the correlation analysis from the Mahalanobis distance analysis and the manifold learning analysis from the method of Laplacian eigenmaps. In addition, the method can be performed on the build-in sensors of the CNC machine tools so that there is no need to change the original structure design to avoid to the potential interferences with the dynamic processing performance, which enables a wide industrial applications of the online real-time health status assessment for the ball screws of the CNC machine tools.

Key words: ball screw, health evaluation, Laplacian eigenmaps, Mahalanobis distance, performance degradation

CLC Number:

TH17

ZHAO Shuai, HUANG Yixiang, WANG Haoren, LIU Chengliang, LIU Xiao, LIANG Xinguang. Laplacian Eigenmaps and Mahalanobis Distance based Health Assessment Methodology for Ball Screw[J]. Journal of Mechanical Engineering, 2017, 53(15): 125-130.

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