A DIAGNOSTIC SYSTEM FOR CYLINDRICAL PLUNGE GRINDING PROCESS BASED ON HILBERT-HUANG TRANSFORM AND PRINCIPAL COMPONENT ANALYSIS

This paper presents a sensor based diagnostic system for a cylindrical plunge grinding process which ensures a reliable process state and tool wear identification. A new signal processing technique, i.e. Hilbert-Huang transform (HHT) was evaluated for this purpose based on the vibration and acoustic emission signal measurements. Numerical and experimental studies have demonstrated that the process state and tool wear may be effectively detected through a statistical analysis of the time-dependent amplitudes and instantaneous frequencies resulting from the HHT. A principal component analysis was used to diagnose different grinding process states.