A Physics-based Model-data-driven Method for Spindle Health Diagnosis, Part I: Modeling of Geometric Faults

Abstract

The spindle determines the performance of machine tools, hence monitoring its health is essential to maintain the machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location is correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyzed the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III.