Machines (Mar 2023)
Wear Parameter Diagnostics of Industrial Milling Machine with Support Vector Regression
Abstract
Modern industrial machine applications often contain data collection functions through automation systems or external sensors. Yet, while the different data collection mechanisms might be effortless to construct, it is advised to have a well-balanced consideration of the possible data inputs based on the machine characteristics, usage, and operational environment. Prior consideration of the collected data parameters reduces the risk of excessive data, yet another challenge remains to distinguish meaningful features significant for the purpose. This research illustrates a peripheral milling machine data collection and data pre-processing approach to diagnose significant machine parameters relevant to milling blade wear. The experiences gained from this research encourage conducting pre-categorisation of data significant for the purpose, those being manual setup data, programmable logic controller (PLC) automation system data, calculated parameters, and measured parameters under this study. Further, the results from the raw data pre-processing phase performed with Pearson Correlation Coefficient and permutation feature importance methods indicate that the most dominant correlation to recognised wear characteristics in the case machine context is perceived with vibration excitation monitoring. The root mean square (RMS) vibration signal is further predicted by using the support vector regression (SVR) algorithm to test the SVR’s overall suitability for the asset’s health index (HI) approximation. It was found that the SVR algorithm has sufficient data parameter behaviour forecast capabilities to be used in the peripheral milling machine prognostic process and its development. The SVR with Gaussian radial basis function (RBF) kernel receives the highest scoring metrics; therefore, outperforming the linear and polynomial kernels compared as part of the study.
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