Tool wear monitoring using acoustic emissions by dominant-feature identification

• Main Authors: Zhou, J.-H., Pang, C.K., Zhong, Z.-W., Lewis, F.L.
• Other Authors: ELECTRICAL & COMPUTER ENGINEERING
• Format: Article
• Published: 2014
• Subjects: Auto-Regressive Moving Average with eXogenous inputs (ARMAX) model; least squares error (LSE); principal component analysis (PCA); principal feature analysis (PFA); singular value decomposition (SVD); tool condition monitoring (TCM)
• Online Access: http://scholarbank.nus.edu.sg/handle/10635/57682
• Institution: National University of Singapore

10.1109/TIM.2010.2050974