ONLINE PRELOAD MONITORING FOR ANTIFRICTION SPINDLE BEATINGS OF HIGH-SPEED MACHINE-TOOLS

Catastrophic and premature bearing failure caused by excessive thermal ly induced bearing preload is a major design problem for spindle beari ngs in highspeed machine tools. Due to a lack of low cost and easy to maintain on-line preload measuring techniques, the traditional solutio n is to limit the maximum spindle speed and the initial bearing preloa d. This solution is incompatible with the trend of high-speed machinin g, which requires increasing both spindle speed and spindle stiffness. Therefore, it would be valuable if thermally-induced preload can be m onitored on-line for regulating bearing thermal behavior at high speed s. This paper proposes using a dynamic state observer based on a prelo ad model to estimate the spindle bearing preload via low cost thermoco uples attached to the bearing outer ring and the spindle housing. The observer is based on a state-space model capable of describing the tra nsient preload behavior of the spindle bearing. The temperatures of th e outer ring and housing are used as the feedback signals for the prel oad observer The observer gains are determined systematically to accou nt for modeling errors, unknown parameters, nonlinearities, and measur ement noise. In particular, the modeling errors due to unexpected fact ors such as bearing skidding, wear, and lubricant deterioration are co mpensated by a Modeling Error Compensator (MEC). By using the MEC, the error dynamics of the obsewer can be converted into a form suitable f or applying existing obsewer techniques such as the Extended Kalman Fi lter (EKF). This preload obsewer has been successfully validated on tw o different bearing configurations operated at different speeds. The r esults show that the model-based monitoring technique, which combines the measurement of outer ring and housing temperature and a robust sta te obsewer, can be an effective and low-cost solution for preload moni toring in high-speed machine tools.