Self-Learning Algorithm for Automated Design of Condition Monitoring Systems for Milling Operations

This paper investigates an approach, termed self-learning ASPS (automated sensor and signal processing selection), aimed at aiding the systematic design of condition monitoring systems for machining operations. The paper outlines a self-learning methodology for the classification of the system’s normal and faulty states and the selection of the most appropriate sensors and signal processing methods for detecting machining faults in end milling. The aim of the proposed approach is to enable the condition monitoring designer to use previous system faults or incidents to design an on-line monitoring system, reducing the system’s development time and cost. Force, acceleration and acoustic emission signals are used to design the condition monitoring systems for end milling operations. Gradual tool wear, catastrophic cutter breakage and tool collision are used for evaluating the proposed self-learning ASPS approach. The initial results show that the suggested algorithm can be applied for an automated, self-learning monitoring system for the selection of the most sensitive sensors and signal processing methods for machining faults and conditions.