Estimating remaining useful life of machine tool ball screws via probabilistic classification

Ball screws are key components in machine tool linear feed drives since they translate the motors' rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw's preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.