Preload Loss Detection in a Ball Screw System Using Interacting Models

Abstract

Ball screw preload is an important factor in maintaining repeatably, rigidity, and in reducing or eliminating backlash in feed drive systems. Ball screw feeds drives are used in computer numerical control (CNC) machine tools to manufacture high-quality, precision parts. Many fault detection and condition monitoring (CM) methods have been proposed for measuring and detecting loss of preload, however, most of these methods require external sensors. Ideally, sensors, measurements, and methods integral to a CNC machine tool could be used to eliminate the extra cost and complexity of external sensors. A sensor-less method of estimating levels of preload using the mode probability of interacting multiple models (IMMs) is proposed. This method calculates a weighted sum which utilizes the mode probability of models representing different levels of preload, along with an activation function and weighing factor, to estimate the current level of preload. Unlike many other methods used for detecting levels of preload, this method requires only a system model and data collected by the CNC systems, while requiring no external sensors. The proposed method was shown to be robust and able to accurately and quickly predict preload levels under many different testing conditions. This method demonstrated a high degree of prediction accuracy (95%) which is comparable to, or better than other methods in the literature. In addition to being a novel method for preload detection, this work is also a novel implementation of IMM for fault detection, as it has not yet been applied to fault detection in feed drives.

Keywords

• Ball screw
• condition monitoring (CM)
• interacting multiple models (IMMs)
• predictive maintenance (PM)
• preload