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Research Spending & Results

Award Detail

Awardee:REGENTS OF THE UNIVERSITY OF MICHIGAN
Doing Business As Name:University of Michigan Ann Arbor
PD/PI:
  • Jun Ni
  • (734) 936-2918
  • junni@umich.edu
Co-PD(s)/co-PI(s):
  • Dragan Djurdjanovic
Award Date:09/13/2006
Estimated Total Award Amount: $ 150,000
Funds Obligated to Date: $ 250,000
  • FY 2006=$90,000
  • FY 2008=$50,000
  • FY 2010=$30,000
  • FY 2009=$80,000
Start Date:09/15/2006
End Date:08/31/2011
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:490100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Industry/University Cooperative Research Center for Intelligent Maintenance Systems (IMS): FIVE-Year Renewal Proposal
Federal Award ID Number:0639468
DUNS ID:073133571
Parent DUNS ID:073133571
Program:IUCRC-Indust-Univ Coop Res Ctr

Awardee Location

Street:3003 South State St. Room 1062
City:Ann Arbor
State:MI
ZIP:48109-1274
County:Ann Arbor
Country:US
Awardee Cong. District:12

Primary Place of Performance

Organization Name:University of Michigan Ann Arbor
Street:3003 South State St. Room 1062
City:Ann Arbor
State:MI
ZIP:48109-1274
County:Ann Arbor
Country:US
Cong. District:12

Abstract at Time of Award

This action continues the life cycle of the multi-university Industry/University Cooperative Research Center for Intelligent Maintenance at the University of Cincinnati, the University of Michigan and the University of Missouri-Rolla. This I/UCRC is in the forefront of research on predictive monitoring and prognostic and decision support tools. The I/UCRC aims to maintain its commitment to intellectual and technical excellence by horizontally fostering stronger international partnerships and vertically deepening its impacts to the current members, as well as to the advancement of scientific knowledge and tools for next-generation autonomous maintenance systems.

Publications Produced as a Result of this Research

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J. Zhou, D. Djurdjanovic, J. Simmons-Ivy and J. Ni "Integration of Maintenance and Reconfiguration Operations for Cost-Effective Maintenance in Reconfigurable Manufacturing Systems" IIE Transactions on Quality and Reliability Engineering, v.39, 2007, p.1085-1102.

L. Li, Q. Chang, J. Ni and S. Biller "Real-time Production Improvement Through Bottleneck Control" International Journal of Production Research, v., 2008, p.. doi:10.1080/00207540802244240 

L. Li, Q. Chang, and J. Ni "Data driven bottleneck detection of manufacturing systems" International Journal of Production Research, v., 2008, p.. doi:10.1080/00207540701881860 

A. Brzezinski, L. Li, X. Qiao, and J. Ni "A New Method for Grinder Dressing Fault Mitigation Using Real Time Peak Detection" International Journal of Advanced Manufacturing Technology, v.45, 2009, p.470.

X. Jin, L. Li and J. Ni "Option Model for Joint Production and Preventive Maintenance System" International Journal of Production Economics, v.119, 2009, p.. doi:10. 1016/j.ijpe.2009.03.005 

S. Ambani, L. Li and J. Ni "Condition-based Maintenance Decision Making for Multiple Machine Systems" ASME Transactions, Journal of Manufacturing Science and Engineering, v.131, 2009, p.031009-1.

J. Liu, D. Djurdjanovic, K. Marko and J. Ni "Growing Structure Multiple Model System for Anomaly Detection and Fault Diagnosis" Transactions of ASME, Journal of Dynamic Systems, Measurements and Control, v., 2009, p..

X. Jin, J. Ni, and Y. Koren "Optimal Control of Reassembly with Variable Quality Returns in a Product Remanufacturing System" CIRP Annals-Manufacturing Technology, v.60, 2011, p.25.

J. Liu and D. Djurdjanovic "Topology Preservation and Cooperative Learning in Identification of Multiple Model Systems" IEEE Transactions on Neural Networks, v.19, 2008, p..

L.Li "Data driven bottleneck detection of complex manufacturing systems" International Journal of Production Research, v., 2008, p.. doi:10.1080/00207540802427894 

Z. Yang, D. Djurdjanovic and J. Ni "Maintenance Scheduling for a Manufacturing System of Machines with Adjustable Throughput" Trans. of IIE, v.39, 2007, p.1111-1125. doi:10.1080/07408170701315339 

L. Li, S. Ambani and J. Ni "Plant-level Maintenance Decision Support System for Throughput Improvement" International Journal of Production Research, v., 2008, p.. doi:10.1080/00207540802375705 

Y. Wang, L. Li, J. Ni and S. Huang "Feature Selection using Tabu Search with Long Memories and Probabilistic Neural Networks" Pattern Recognition Letters, v.30, 2009, p.661.

Y. Wang, L. Li, J. Ni and S. Huang "Feature Selection using Tabu Search with Long Memories and Probabilistic Neural Networks" Pattern Recognition Letters, v.30, 2009, p.661.

Y. Lei, D. Djurdjanovic, J. Ni, J. Lee, G. Xiao and J. R. Mayor "System Level Optimization of Predictive Maintenance in Industrial Automation Systems" Transactions of NAMRI/SME, v.34, 2006, p.79-86.

L. Li and J. Ni "Short-term Decision Support System for Maintenance Task Prioritization" International Journal of Production Economics, v., 2009, p.. doi:DOI: 10.1016/j.ijpe.2009.05.006 

Y. Wang, L. Li, J. Ni and S. Huang "Form Tolerance Evaluation of Toroidal Surfaces Using Particle Swarm Optimization" ASME Transactions, Journal of Manufacturing Science and Engineering, v.131, 2009, p.051015:1.

Z. Yang, Q. Chang, D. Djurdjanovic, J. Ni and J. Lee "Maintenance Priority Assignment On-Line Production Information" ASME J. of Manuf. Science and Engineering, v.129, 2007, p.435-446.

L. Li, M. You and J. Ni "Reliability-based Dynamic Maintenance Threshold for Failure Prevention of Continuously Monitored Degrading Systems" ASME Transactions, Journal of Manufacturing Science and Engineering, v.131, 2009, p.031010-1.

L. Li, and J. Ni "Reliability Estimation Based On Operational Data of Manufacturing Systems" Quality and reliability engineering international, v.24, 2008, p.843.

Y. Lei, D. Djurdjanovic, L. Barajas, G. C. Workman, J. Ni and S. Biller "Network Health Monitoring for DeviceNet Using Physical Layer" Journal of Intelligent Manufacturing, v., 2009, p..

M. You, L. Li, G. Meng and J. Ni "Cost-Effective Updated Sequential Predictive Maintenance Policy for Continuously Monitored Degrading Systems" IEEE Transactions on Automation Science and Engineering, v., 2010, p.. doi:10.1109/TASE.2009.2019964 

S. Lee, L. Li, and J. Ni "Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model" ASME Transactions, Journal of Manufacturing Science and Engineering, v.132, 2010, p.021010: 1.

S. Lee, L. Li, and J. Ni "Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model" ASME Transactions, Journal of Manufacturing Science and Engineering, v.132, 2010, p.021010: 1.

M. You, L. Li, G. Meng and J. Ni "Two-zone Proportional Hazard Model for Equipment Reliability Assessment and Remaining Useful Life Estimation" ASME Transactions, Journal of Manufacturing Science and Engineering, v., 2010, p..


Project Outcomes Report

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The Center for Intelligent Maintenance Systems (IMS) was established in 2001 as an NSF Industry/University Cooperative Research Center (I/UCRC), through a partnership among the Missouri University of Science and Technology, the University of Cincinnati and the University of Michigan. To date, the Center has conducted over 70 research projects with over 50 member companies and sponsors. The Center addresses the underlying issues in predictive monitoring and prognostic, and system-level maintenance decision support tools. Over the past 10 years, the Center has developed systematic methodologies and tools that made evident impacts to a number of member companies including Eaton, GM, Ford, Chrysler, and BorgWarner, amongst others. 
Research topics include:

  • Battery prognostics and its management system
  • Analysis of remanufacturing system with EV batteries
  • Degradation-based battery management system for vehicle fleet companies
  • Study on Maintenance Opportunity Windows for production systems
  • Data-driven short-term bottleneck detection for intelligent maintenance decision-making

The Center intends to advance the scientific base as well as to validate the developed tools to further accelerate the deployment and commercialization of the developed technologies. For example, our Center has worked in a successful collaboration with GM for more than ten years and two students from our Center have been hired by GM. As part of our effort with GM, our collaborators have received two “Boss” Kettering awards. We also worked with BorgWarner to reduce tooling cost on a shaving process by employing condition-based maintenance. As part of this project, we were able to demonstrate a potential cost-savings of more than $11,000 per tool per machine.

 


Last Modified: 12/14/2011
Modified by: Jun Ni

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