Design of a reconfigurable prognostics platform for machine tools

Authors:
Linxia Liao;Jay Lee
Affiliations:
NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, Univ. of Cincinnati, Cincinnati, OH 45221, USA;NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, Univ. of Cincinnati, Cincinnati, OH 45221, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
Cited 1

Measurement of machine performance degradation using a neural network model

Computers in Industry - Special issue: computer integrated manufacturing (ICCIM '95)
A review of web-based product data management systems

Computers in Industry
Intelligent prognostics tools and e-maintenance

Computers in Industry - Special issue: E-maintenance
Development of an e-maintenance system integrating advanced techniques

Computers in Industry - Special issue: E-maintenance
Development and application of internet-based remote monitoring and diagnostic platform for key equipment

International Journal of Computer Integrated Manufacturing - Networked Manufacturing and Mass Customization in the ECommerce Era: the Chinese Perspective

Proactive control of manufacturing processes using historical data

KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II

Quantified Score

Hi-index	12.05

Visualization

Abstract

For decades, researchers and practitioners have been trying to develop and deploy prognostics technologies with ad hoc and trial-and-error approaches. These efforts have resulted in limited success, due to the fact that it lacks a systematic approach and platform in deploying the right prognostics tools for the right applications. This paper introduces a methodology for designing a reconfigurable prognostics platform (RPP) which can be easily and effectively used to assess and predict the performance of machine tools. RPP can be installed on the equipment and it has the prognostic capabilities to convert the data into performance-related information. The equipment performance information can then be integrated into the enterprise asset management system for maintenance decision making through the Internet. Two industrial cases are used to validate the effectiveness of applying the RPP for different prognostic applications as well as the reconfigurable capabilities of the proposed RPP.