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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF CINCINNATI
Doing Business As Name:University of Cincinnati Main Campus
PD/PI:
  • Jay Lee
  • (513) 556-2493
  • jay.lee@uc.edu
Award Date:09/13/2006
Estimated Total Award Amount: $ 315,000
Funds Obligated to Date: $ 844,196
  • FY 2008=$161,000
  • FY 2009=$172,951
  • FY 2010=$172,902
  • FY 2007=$25,000
  • FY 2006=$189,000
  • FY 2011=$115,343
  • FY 2012=$8,000
Start Date:09/15/2006
End Date:08/31/2014
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:0639469
DUNS ID:041064767
Parent DUNS ID:041064767
Program:IUCRC-Indust-Univ Coop Res Ctr
Program Officer:
  • Thyagarajan Nandagopal
  • (703) 292-4550
  • tnandago@nsf.gov

Awardee Location

Street:University Hall, Suite 530
City:Cincinnati
State:OH
ZIP:45221-0222
County:Cincinnati
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:University of Cincinnati Main Campus
Street:University Hall, Suite 530
City:Cincinnati
State:OH
ZIP:45221-0222
County:Cincinnati
Country:US
Cong. District:01

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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Siegel, D., Zhao, W., Lapira, E., AbuAli, M. and Lee, J. "A Comparative Study on Vibration ? Based Condition Monitoring Algorithms for Wind Turbine Drive Trains" Wind Energy Journal (Special Issue), v., 2013, p..

Liu, C.L., Lee, J., Gong, J., Huang, Y.X. "Prognostics and Maintenance for Mechanical Systems in Harsh Environment" Advances in Mechanical Engineering, v.2013, 2013, p.121340.

Yu, G; Qiu, H; Djurdjanovic, D; Lee, J "Feature signature prediction of a boring process using neural network modeling with confidence bounds" INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v.30, 2006, p.614. doi:10.1007/s00170-005-0114-  View record at Web of Science

Zhao, W., Siegel, D., Lee, J., Su, L. "An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines" International Journal of the PHM Society, v.4, 2013, p..

Siegel, D. and Lee, J. "n Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment" International Journal of Prognostics and Health Management Society, v.2 (2) 0, 2011, p.12.

Zhang, L., Cao, Q.X., Lee, J. "A novel ant-based clustering algorithm using Renyi entropy" Journal of Advanced Soft Computing, v., 2013, p..

Huang, Y., Zha, X., Lee, J. and Liu, C. "Discriminant Diffusion Maps Analysis: A Robust Manifold Learner for Dimensionality Reduction and its Applications in Machine Condition Monitoring and Fault Diagnosis" Mechanical Systems and Signal Processing, v., 2013, p..

Lee, J., Liao, H.T. "Predictive Monitoring and Failure Prevention of Vehicle Electronic Components and Sensor Systems" Transaction of SAE, v., 2006, p.. doi:2006-01-0373 

Jawad Raza, Jayantha P. Liyanage Hassan Al Atat, Jay Lee "A comparative study of maintenance data classification based on neural networks, logistic regression and support vector machines" Journal of Quality in Maintenance Engineering, v.16-3, 2010, p..

Siegel, D., Ly, C. and Lee, J. "Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure" IEEE Transactions on Reliability, v.61, 2012, p..

Lee J, Lapira E, Bagheri B, Kao HA, "Recent advances and trends in predictive manufacturing systems in big data environment" Manufacturing Letters, v.1, 2013, p..

Zhang, J. and Lee, J. "A review on prognostics and health monitoring of Li-on battery" Journal of Power Sources, v.196(15), 2011, p.6007.

Yang, L. and Lee, J. "Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems" Robotics and Computer-Integrated Manufacturing, v.28, 2012, p.66.

Song, B.L., Lee, J. "Framework of Designing an Adaptive and Multi-Regime Prognostics and Health Management for Wind Turbine Reliability and Efficiency Improvement" International Journal of Advanced Computer Science and Applications, v.4, 2013, p..

Xia, T., Xi, L, Lee, J. and Zhou, X. "Optimal CBPM policy considering maintenance effects and environmental condition" International Journal of Advanced Manufacturing Technology, v.56 (9-1, 2011, p.1181.

Zhao, W., Siegel, D., Lee, J. and Su, L., "An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines" International Journal of the PHM Society, v.4, 2013, p..

Zhang, L., Cao, Q.X., Lee, J. "Performance Assessment for a Fleet of Machines Using a Combined Method of Ant-Based Clustering and CMAC" Advances in Mechanical Engineering, v.2013, 2013, p.603071.

Lee, J; Ni, J; Djurdjanovic, D; Qiu, H; Liao, HT "Intelligent prognostics tools and e-maintenance" COMPUTERS IN INDUSTRY, v.57, 2006, p.476. doi:10.1016/j.compind.2006.02.01  View record at Web of Science

Lee, J. and AbuAli, M. "Innovative Product Advanced Service Systems (i-PASS): Methodology, tools and applications for dominant service design" International Journal of Advanced Manufacturing Technology, v.52 (9-1, 2011, p.1161.

Zhou, Xiaojun, Xi, Lifeng, Lee, J., "A Dynamic Opportunistic Maintenance Policy for Continuously Monitored Systems" Journal of Quality in Maintenance Engineering, v.Vol. 12, 2006, p..

Al-Atat, H.; Siegel, D.; Lee, J. "A Systematic Methodology for Gearbox Health Assessment and Fault Classification" International Journal of Prognostics and Health Management, v., 2011, p..

Wu, F.; Wang, T.; Lee, J. "An online adaptive condition-based maintenance method for mechanical mystems" Mechanical Systems and Signal Processing, v.24, 2010, p.2985.

Lee, J., Ghaffari, M. and Elmellgy, S. "Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems" Annual Reviews in Control, v.35 (1), 2011, p.111.

Wang, S., Yu, J., Lapira, E. and Lee, J. "A Modified Support Vector Data Description based Novelty Detection Approach for Machinery Components" Applied Soft Computing, v., 2013, p..

Kao, H.A, Jin, Siegel, D., and Lee, J. "Cyber Physical Interface for Automation Systems - Methodology and Example" Journal of Machines, v.2014, 2014, p..

Huang, Y., Zha, X., Lee, J. and Liu, C. "Discriminant Diffusion Maps Analysis: A Robust Manifold Learner for Dimensionality Reduction and its Applications in Machine Condition Monitoring and Fault Diagnosis" Mechanical Systems and Signal Processing, v.34, 2013, p..

Liao, L., Lee J. "Designing a Reconfigurable e-Prognostics Platform for Machine Tools" Expert Systems with Applications, v.37(1), 2010, p.240.

Lee, J., Wu, F., Zhao, W.Y., Ghaffari, M., Wang, T.Y., Siegle, D. "Prognostics and Health Management Design for Rotary Machinery Components" International Journal of Mechanical Systems and Signal Processing, v., 2013, p..

Siegel, D., Zhao, W., AbuAli, M., and Lee, J. "A comparative study on vibration ? based condition monitoring algorithms for wind turbine drive trains" Wind Energy Journal, v., 2013, p..

Liao, L., Lee J. "A Novel Method for Machine Performance Degradation Assessment based on Fixed Cycle Features Test" Journal of Sound and Vibration, v.326(3-5, 2009, p.894.

Wu, F. and Lee, J. "Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals" International Journal of the PHM Society, v.2(1) 00, 2011, p.9.

Siegel, D., Al-Atat, H., Shauche, V., Liao, L., Snyder, J., Lee, J. "Novel method for Rolling Element Bearing Health Assessment â?? A Tachometer-less Synchronously Averaged Envelope Feature Extraction Technique" Mechanical Systems and Signal Processing, v.29, 2012, p.362.

Lee, J. "Transforming Productivity from Product-based Manufacturing to Service-Centric Innovation" Japanese Productivity Center Technology Innovation Management (TiM) Journal, v.June, 2006, p..

Jin, C., Ompusunggu, AP, Liu, ZC, Ardakani, HD, Petre, F, Lee, J. "A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis" Journal of PHM, v.2014, 2014, p..

Yang, ZM; Chang, Q; Djurdjanovic, D; Ni, J; Lee, J "Maintenance priority assignment utilizing on-line production information" JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, v.129, 2007, p.435. doi:10.1115/1.233625  View record at Web of Science

Xia, Xi, Zhou, Lee, "Condition-based maintenance for intelligent monitored series system with independent machine failure modes" International Journal of Production Research, v.May, 2013, p..

Chen, Y. and Lee, J. "Data Quality Evaluation and Improvement for Prognostic Modeling using Visual Assessment based Data Partitioning Method" Computers in Industry, v., 2013, 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.

IMS Center

During the past year, the IMS Center has made great strides in a number of areas:

Accelerated Life Testing (ALT)

IMS Center has initiated the design of a systematic framework through which the data-driven and physics-based models for Prognostics and Health Management (PHM) can be integrated. This research investigates the advantages and limitations of both modeling approaches and helps to establish a more effective and robust PHM approach by integrating them. The Center’s work on accelerated life testing (ALT) has enabled the rapid development and validation of these modeling approaches. 

Cyber-Physical Systems (CPS)

IMS Center is establishing a Cyber-Physical System (CPS) framework, which leverages the Center’s patented Peer-to-Peer (P2P) prognostics methodology (US – 13/674,200), to enable the factories to deploy transformative technologies and advanced analytics to connect their physical assets and establish virtual models to improve their efficiency and resilience. This move towards resilient systems is informed by the Center’s work on the NSF Engineering Immune Systems Fundamental Research project (EIS) (NSF IIP-0639469-009).

Data Quality

The Center’s ongoing work the NSF GOALI Research Project on “A Systematic Methodology for Data Validation and Verification for Prognostics Applications” seeks to ensure that data acquired meets the requirements for use in prognostics applications. The methods established during this project has been utilized in a number of industry funded projects, such as the ALSTOM Transport Track Tracer project and a project with a local Cincinnati utility.

Minimal-sensing Techniques

The Center has worked on establishing minimal-sensing techniques for PHM of machines and processes during transient operating conditions. With the possible difficulties and the costs associated with the instrumentation for PHM purposes, industries are willing to explore the possibility of using currently available resources for deploying PHM solutions without investing on new instrumentation equipment. On the other hand, the reliability of the PHM solutions at all of the working regime profiles of assets has been a concern for industry. 

IMS Center Cloud-based Mobile Application

The IMS Center's Mobile Health Monitoring Application can provide users with fexibility as well as functionality. This App presents a global view of the user's facility or production line while also allowing the user to drill down to see the health status of each individual component being monitored. 


Last Modified: 01/05/2015
Modified by: Jay Lee

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