SMRP BEST PRACTICES METRICS WORKSHOP

Operations & Maintenance

Best Practices

A Guide to Achieving Operational Efficiency

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G. P. Sullivan

R. Pugh

A. P. Melendez

W. D. Hunt

December 2002

Prepared by

Pacific Northwest National LaboratoryUnder Contract DE-AC06-76RL01831for the Federal Energy Management Program

U.S. Department of Energy

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Best Practices Framework for Improving Maintenance Data Quality

to Enable Asset Performance Analytics

Sarah Lukens1, Manjish Naik2, Kittipong Saetia3, and Xiaohui Hu4

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1,2,3,4GE Digital, San Ramon, CA, 24013, USA

sarah.lukens@ge.com

manjish.naik@ge.com

kittipong.saetia@ge.com

xiaohui.hu@ge.com

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A Multiple Model Prediction Algorithm for CNC Machine Wear PHM

Huimin Chen 1

1 Department of Electrical Engineering, University of New Orleans, New Orleans, LA, 70148, U. S. A.

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hchen2@uno.edu

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Annual Conference of the Prognostics and Health Management Society – PHM Data Challenge 2018

PHM DATA CHALLENGE 2018

A Collaborative Data Library for Testing Prognostic Models

Joanna Sikorska1, Melinda Hodkiewicz2, Ashwin D’Cruz3, Lachlan Astfalck4, and Adrian Keating5

1CASWA, 2/33 Horus Bend, Bibra Lake, Perth, Western Australia, 6163, Australia

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(jo@caswa.com)

2 The University of Western Australia, School of Mechanical and Chemical Engineering, Mail Bag M050, Western Australia, 6009, Australia, (melinda.hodkiewicz@uwa.edu.au)

3 The University of Western Australia, School of Mechanical and Chemical Engineering, Mail Bag M050, Western Australia, 6009, Australia, (ashwin.dcruz@uwa.edu.au)

4 The University of Western Australia, School of Mechanical and Chemical Engineering, Mail Bag M050, Western Australia, 6009, Australia, (Lachlan.astfalk@uwa.edu.au)

5 The University of Western Australia, School of Mechanical and Chemical Engineering, Mail Bag M050, Western Australia, 6009, Australia, (adrian.keating@uwa.edu.au)

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REVIEW OF DATA-DRIVEN PROGNOSTICS AND HEALTH MANAGEMENT TECHNIQUES: LESSIONS LEARNED FROM PHM DATA CHALLENGE COMPETITIONS

Bin Huang, Yuan Di, Chao Jin and Jay Lee

NSF I/UCRC for Intelligent Maintenance Systems (IMS)

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University of Cincinnati

560 Baldwin Hall

University of Cincinnati

Cincinnati, OH 45221

Telephone: (513) 556-4650

huangba@mail.uc.edu

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Review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics

Xiaodong Jia1, Bin Huang2, Jianshe Feng3, Haoshu Cai4, Jay Lee5

1,2,3,4,5 NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, Ohio 45221-0072, USA

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jiaxg@mail.uc.edu

huangba@mail.uc.edu

fengje@mail.uc.edu

caihu@mail.uc.edu

lj2@ucmail.uc.edu

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