A Rough Set-Based Effective State Identification Method of Multisensor Tool Condition Monitoring System

Abstract

Multisensor improves the accuracy of machine tool condition monitoring system, which provides the critical feedback information to the manufacture process controller. Multisensor monitoring system needs to collect abundant data to employ attribute extraction, election, reduction, and classification to form the decision knowledge. A machine tool condition monitoring system has been built and the method of tool condition decision knowledge discovery is also presented. Multiple sensors include vibration, force, acoustic emission, and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multisensor and machining parameters and then extracts a set of minimal state identification rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool conditions are identified. A case study is presented to illustrate that the approach produces effective and minimal rules and provides satisfactory accuracy.