Feature selection for tool wear monitoring: A comparative study

One of the challenging tasks in the domain of Tool Condition Monitoring (TCM) is feature selection. Feature selection is crucial as extracting all possible features and creating a model based on those features results in two major disadvantages, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. In this paper, four statistical feature selection methods are applied to the TCM problem in a CNC-milling machine. These methods are Ridge Regression (RR), Principal Component Regression (PCR), Least Absolute Shrinkage and Selection Operator (LASSO), and Fisher's Discriminant Ratio (FDR). Applicability of these methods are compared based on their diagnostic results in two cases using a single Hidden Markov Model (HMM) approach.