Tool Condition Monitoring With Multiscale Discriminant Sparse Decomposition

Sparse decomposition has been successfully applied in manufacturing process condition monitoring. In the sparse decomposition-based approaches, the representation dictionary plays an important role. However, most current dictionary learning based methods do not consider the atoms corresponding to the specific states in the dictionary, and the coding coefficients lack discrimination between different states. This study develops an approach capable of multiscale dictionary learning for tool condition monitoring (TCM) and improving the discrimination power by maximizing the between-class and within-class coding coefficients. In addition, through the use of the discriminant distance without signal reconstruction, it enhances the state discrimination and increases the speed of online monitoring. The experimental results have validated the effectiveness of the approach with applications to micromilling TCM.