Using Tracking Analysis and Predictive Maintenance in Order to Obtain Dynamics of Machine Tool Spindle

TL;DR

This paper explores using tracking analysis and vibration data to determine the eigenvalue frequencies of machine tool spindles, aiming to enhance predictive maintenance by early detection of machinery condition changes.

Contribution

It introduces a procedure to obtain eigenvalue frequencies of machine tool spindles through tracking analysis, advancing predictive maintenance techniques.

Findings

Successful identification of eigenvalue frequencies using tracking analysis.

Enhanced early detection capabilities for machine spindle condition.

Potential for improved predictive maintenance strategies.

Abstract

Predictive maintenance is directed towards recognizing the earliest significant changes in machinery condition. Contrasted with protective condition monitoring in which fast response is the primary requirement, predictive monitoring is not limited by time and may use a greather range of complex characteristics. Vibration analysis has long been used for the detection and identification of machine tool condition. Main focus is to identify a procedure to obtain eigenvalue frequencies for machine tool spindle using tracking analysis.