SUMMARY

Bearing diagnostics provide valuable information related to a bearing's health and enables Condition Based Maintenance (CBM) for rotary machines. This is an effective way to decrease unnecessary cost and downtime caused by unanticipated machine spindle failure. However, techniques to effectively evaluate bearing damage severity from these extracted features are still a significant challenge. Moreover, a gap still exists between these advanced diagnostics approaches and their application in production operations. The objective of this thesis is to perform bearing diagnostics on a machine tool under operating conditions for a spall-like defect on the bearing races. The defect size will be estimated while cutting to evaluate the severity of the damage.