MACHINE TOOL FEED AXIS HEALTH MONITORING USING PLUG-AND- PROGNOSE TECHNOLOGY

摘要

Operational safety, maintenance, cost effectiveness, and asset availability have a direct impact on the competitiveness of organizations. In order to address the issues associated with the maintenance related machine downtime, various maintenance strategies have been adopted over the years. One of the most desirable approaches is condition based maintenance (CBM). Machine tools are highly complex and their systems are very often subjected to varying loads and working conditions that make health monitoring and assessment strategies difficult to implement. Siemens Corporate Research & Technology, a division of Siemens Corporation, is developing a Plug-and-Prognose (PnP) technology to monitor the health of production type machine tools. Siemens partnered with TechSolve to evaluate and validate the technology through a series of tests focused on the ability of the PnP system to effectively collect data from the machine tool's own controller and external sensors, and reliably identify the normal operation of the machine and diagnose anomalous operating states. Experimental trials conducted on the TechSolve's feed-axis test-bed and the DMU50 machine demonstrated the effectiveness of PnP technology for anomaly detection and diagnosis. A few practical issues and more experience about test design, findings, and issues encountered through the experiment are shared and discussed as well.