SKF Reliability Systems - Library
SKF Reliability Systems - Library
SKF Reliability Systems - Library
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13<br />
Vol 23 No 3 AMMJ Intelligent Maintenance <strong>Systems</strong><br />
4.2. Application on Factory Energy Services<br />
This is an on-going project that aims on establish a Precision Energy Management <strong>Systems</strong> (PEMS) to<br />
increase factory energy transparency and awareness as well aid in reduction of electrical energy consumption<br />
levels by linking IMS tools and techniques to company products, processes, and business level. There are<br />
three key objectives:<br />
• PRODUCT: the PEMS must be able to define measure, analyze, and predict energy consumption per<br />
product manufactured, in such a way that each product leaving the process line can have an “Energy Label”<br />
that resembles the amount of energy consumed in manufacturing this product.<br />
• PROCESS: the PEMS must be able to use the developed energy metrics and parameters for detailed<br />
operational analytics. In other words, the PEMS will assess both equipment-level and process-level degradation<br />
and integrity. Such a correlation between maintenance activities on the shop floor and energy consumed<br />
through the machines and processes is an essential part of the PEMS initiative and is a large gap in today’s<br />
industry operations.<br />
• PLANT: the PEMS will enable facility-wide energy transparency, so that manufacturers can expand their<br />
current performance metrics of optimized quality, cost, and lead time to incorporate energy. Modeling and<br />
optimization of energy costs can also lead to improved manufacturing resource utilization.<br />
5 CONCLUSIONS<br />
This paper presented the concept of intelligent maintenance systems as well as a systematic methodology<br />
and tools that have been effectively utilized to implement novel and innovative maintenance service systems<br />
in a diverse set of industries. The paper also presented two case studies to highlight the usage of intelligent<br />
maintenance systems in different industries.<br />
In conclusion, the benefits of an intelligent maintenance system will deliver new service functions to different<br />
customers for different products. Predictive maintenance services of assets will enable better information flow<br />
for better decision making. For a manufacturing business, predictive maintenance service of equipment will<br />
extend the life of equipment, increase uptime, increase product quality, decrease costs and increase overall<br />
productivity. For the product designer, predictive maintenance service of the designed products will enable a<br />
collaborative product closed-loop life cycle management.<br />
ACKNOWLEDGEMENTS<br />
NSF Industry/University Cooperative Research Center has been supported by NSF since 2001. Since its<br />
inception, it has been supported by over 60 companies globally, including P&G, GE Aviation, Toyota, GM,<br />
Caterpillar, Nissan, Parker Hannifin, Harley Davidson, AMD, Siemens, CISCO, Omron, Toshiba, Komatsu,<br />
Syncrude, Spirit Aerosystems, National Instruments, ITRI Taiwan, PMC, Advantech, TechSolve, Kistler, API,<br />
Army Research Lab., Intel, USPS, Hitachi, Rockwell Automation, etc. This support provided a solid foundation<br />
for IMS development and implementation.<br />
REFERENCES<br />
[1] Center for Intelligent Maintenance <strong>Systems</strong> (IMS), http://www.imscenter.net<br />
[2] Hai Q, Lee J, (2007) ‘Near-zero downtime: Overview & Trends’, Reliable Plant Magazine & Lean Manufacturing<br />
Journal<br />
[3] Lee J (1998) ‘Teleservice Engineering in Manufacturing: Challenges and Opportunities,’ International Journal of Machine<br />
Tools & Manufacture, Vol. 38, Number 8, pp. 901-910.<br />
[4] Lee J et al. (2003) ‘Watchdog Agent: Infotronics-based Prognostics Approach for Product Performance Degradation<br />
Assessment and Prediction.’ Advanced Engineering Informatics (2003)<br />
[5] Lee J (2003) ‘Smart Products and Service <strong>Systems</strong> for e-Business Transformation,’ Special Issues on “Managing<br />
Innovative Manufacturing,” International Journal of Technology Management, pp. 45-52, Vol. 26, No. 1.<br />
[6] Lee J (2003) ‘e-Manufacturing <strong>Systems</strong>: Fundamentals and Tools,’ Int. Journal of Robotics and Computer-integrated<br />
Manufacturing, Vol 9. Issue 6, pp 501-507.<br />
[7] Lee J, Ni J, Djurdjanovic D, Qui H., Liao H, (2006) ‘Intelligent Prognostics Tools and E-Maintenance,’ Int. Journal of<br />
Computer in Industry, Volume 57, Issue 6.<br />
[8] Lee J (2008). Dominant Innovation Design for Product and Service <strong>Systems</strong>. PowerPoint Lecture Course Presentations.<br />
University of Cincinnati, Cincinnati, OH. Sept-Dec 2008.<br />
[9] Lee J, AbuAli M, Deng C, Tsan C (2009) ‘Product Lifecycle Management Using Embedded Infotronics: Methodology,<br />
Tools, and Case Studies’, Int. J. Knowledge Engineering and Data Mining (IJKEDM), (Accepted)<br />
[10] Lee J, Yan C, Lapira E, Al-Atat H, and AbuAli M (2009) ‘A Systematic Approach for Predictive Maintenance<br />
Service Design: Methodology and Applications’, Int. J. Internet Manufacturing Services (IJIMS), (Accepted)<br />
The full Proceedings of COMADEM 2009 are available for sale. Please contact aarnaiz@tekniker.es