Health Indicator Analysis in Terms of Condition Monitoring on Brownfield CNC Milling Machines Using Triaxial Accelerometer

Abstract

Evaluating the quality of the machining process annotated by experts on the floor in case of developing a silent anomaly is a challenging task. Components wear, wrongly labeled processes, or highly imbalanced data are some examples of real-world difficulties that may prevent the reliability of machine learning algorithms in the manufacturing environment. Since human experts may face several challenges while annotating such high-frequency data, this letter evaluates effective health indexes using time-frequency analysis to extract reliable patterns or vibration signatures assigned to the process quality or bearing health status. A benchmark dataset for process monitoring of Brownfield milling machines over two years is utilized in this letter where the resulting process is evaluated by experts in a gauging station. Vibration signals are collected from three different computer numerical control (CNC) using a triaxial accelerometer, which is mounted on the rear side of the machines. Considering a single operation, the extracted vibration signature is validated on two test CNC machines. As results show, the overall energy level in the frequency range of 0-1 kHz while considering only radial axes gives effective insight into the quality of the process and degradation pattern.