A Cyber Physical Interface for Automation Systems—Methodology and Examples

Cyber physical systems (CPS) in a manufacturing and automation context can be referred to different manufacturing process, including design, simulation, control, and verification. However, for data analytics, the concept of CPS is relatively new, and a standard methodology is lacking on how to incorporate this type of interface for automation applications. This study discusses a modeling methodology for a cyber physical interface and presents the five levels of information for a cyber physical system, that range from the data connection level to the system configuration level. In order to achieve this awareness and health state of the machine and system, a technical approach that uses adaptive health monitoring algorithms is presented. Lastly, an experimental study on a machine tool ball screw is highlighted, in which a predictive model and a cyber physical interface is developed for this application. The outcomes from this study demonstrate that machine health state awareness is feasible, and the core technologies can aim mechanical systems systematically develop its CPS. This can lead to additional product revenue for the manufacturers, and also a potential competitive edge in the market place.

[1]  Jennifer E. Rowley,et al.  The wisdom hierarchy: representations of the DIKW hierarchy , 2007, J. Inf. Sci..

[2]  Yung-Chou Kao,et al.  An agent-based distributed smart machine tool service system , 2010, 2010 International Symposium on Computer, Communication, Control and Automation (3CA).

[3]  Jun Ni,et al.  Short-term decision support system for maintenance task prioritization , 2009 .

[4]  P. S. Neelakanta,et al.  Robust factory wireless communications: a performance appraisal of the Bluetooth/spl trade/ and the ZigBee/spl trade/ colocated on an industrial floor , 2003, IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).

[5]  Michael G. Pecht,et al.  An Options Approach for Decision Support of Systems With Prognostic Capabilities , 2012, IEEE Transactions on Reliability.

[6]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[7]  Gabor Karsai,et al.  Toward a Science of Cyber–Physical System Integration , 2012, Proceedings of the IEEE.

[8]  Min-Hsiung Hung,et al.  Development of a web-services-based e-diagnostics framework for semiconductor manufacturing industry , 2005 .

[9]  Linxia Liao,et al.  MACHINE TOOL FEED AXIS HEALTH MONITORING USING PLUG-AND- PROGNOSE TECHNOLOGY , 2012 .

[10]  Antonio Pflüger,et al.  Executive Summary. , 2012, Journal of the ICRU.

[11]  Martin Frické,et al.  The knowledge pyramid: a critique of the DIKW hierarchy , 2009, J. Inf. Sci..

[12]  Helen Gill,et al.  Cyber-Physical Systems , 2019, 2019 IEEE International Conference on Mechatronics (ICM).

[13]  Daniela L. Nastasie,et al.  INTEGRATION THROUGH STANDARDS – AN OVERVIEW OF INTERNATIONAL STANDARDS FOR ENGINEERING ASSET MANAGEMENT , 2007 .

[14]  K. Shadan,et al.  Available online: , 2012 .

[15]  Francesco De Pellegrini,et al.  On the use of wireless networks at low level of factory automation systems , 2006, IEEE Transactions on Industrial Informatics.

[16]  Jiafu Wan,et al.  A survey of Cyber-Physical Systems , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[17]  Natarajan Shankar,et al.  Mitigation of Chatter Instabilities in Milling by Active Structural Control , 2001 .

[18]  Jay Lee,et al.  Fault detection in a network of similar machines using clustering approach , 2012 .

[19]  Jun Ni,et al.  Decision support systems for effective maintenance operations , 2012 .

[20]  Wayne H. Wolf,et al.  Cyber-physical Systems , 2009, Computer.

[21]  Noureddine Zerhouni,et al.  CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks , 2012 .

[22]  N. Iyer,et al.  Framework for post-prognostic decision support , 2006, 2006 IEEE Aerospace Conference.

[23]  Yu-Wei Su,et al.  A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[24]  Jianjun Shi,et al.  Active Balancing and Vibration Control of Rotating Machinery: A Survey , 2001 .

[25]  H. Van Brussel,et al.  Non-linear dynamics tools for the motion analysis and condition monitoring of robot joints , 2001 .