Dimensions reduction of vibration signal features using LDA and PCA for real time tool wear detection with single layer perceptron

Abstract

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.