[J6] Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients

Identification of material type in structural cracks is crucial for fault diagnosis and structural integrity assessment in multi-component operation systems. Acoustic emission (AE) signal analysis has been extensively explored as an effective method for crack monitoring and fatigue damage assessment. Most existing research on crack diagnosis is primarily focused on determining the crack formation in structures using the AE signals corresponding to known materials. However, the material recognition at any given cracking conditions using AE signals has lagged behind, hindering fault detection and damage repair in complex and multi-material structures. This research investigates the feasibility of the synergistic use of Mel Frequency Cepstral Coefficients (MFCC) and K-nearest neighbor (KNN) algorithms for identifying the material type in any arbitrary cracking processes.