Open AccessDimensions reduction of vibration signal features using LDA and PCA for real time tool wear detection with single layer perceptron
Author(s) -
Anis Arendra,
Herianto Herianto,
Akhmad Sabarudin,
Ida Lumintu
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1125/1/012052
Subject(s) - perceptron , linear discriminant analysis , principal component analysis , pattern recognition (psychology) , artificial intelligence , dimensionality reduction , multilayer perceptron , computer science , classifier (uml) , feature extraction , vibration , artificial neural network , speech recognition , physics , quantum mechanics
This study uses the Linear Discriminant Analysis (LDA) method along with the Principal Component Analysis (PCA) method to reduce the dimensionality of the vibration signal feature classified by Single Layer Perceptron (SLP). The vibration features to be reduced are 10 out of 270 features selected based on the correlations analysis. The LDA and PCA transformations provide only three inputs, than the original 10 signal features for the SLP classifier. The Single Layer Perceptron is trained with a sequential incremental training approach using the perceptron learning rule. The training phase of SLP resulting Mean Squared Error (MSE) as low as 0.0840 and the validation phase in the CNC machining provides 97.5% accuracy with zero false alarms.