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.