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Collective Mind: Continuous, Automatic Learning to Improve Equipment Maintenance A Request for Guidance CBM+ February 11, 2005 Norman Sondheimer Al Wallace.

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Presentation on theme: "Collective Mind: Continuous, Automatic Learning to Improve Equipment Maintenance A Request for Guidance CBM+ February 11, 2005 Norman Sondheimer Al Wallace."— Presentation transcript:

1 Collective Mind: Continuous, Automatic Learning to Improve Equipment Maintenance A Request for Guidance CBM+ February 11, 2005 Norman Sondheimer Al Wallace Peter Will UMass Amherst RPI USC/ISI

2 Vision: New Paradigm for Maintenance Decision Support Objective: Actively Manage the Maintenance Process – Continuously Improve Planning, Response, and Execution – Operating at All Levels, All Phases of Operations – Linking all Elements into a Living, Distributed, Global Maintenance System Basic Building Block: Self-Aware Platform Global Community of Continuously Improving Equipment Sense and Respond Maintenance Network Technical Approach... Collective Mind: Communities of Self-Aware Platforms

3 Self-Sustainment Requirement  Army Future Force units in 2020  Air Expeditionary Force  Navy/Marines Sea Basing Critical Capabilities: Reliability Prognostics Army: “The UA is self-sustainable for 3-7 days of operations and maintains combat power with dramatically reduced theater stockpiles.”

4 Challenges for Prognostics  Lack of Physics of Failure models  Missing sensor suites  Low equipment-utilization rates  etc. But Maintenance Crews  Have the ability to improve reliability of their equipment over time We have yet to tap the data we have!

5 Claim: Existing field experience can be used to improve Prognostics  Discover similar units Peers form a “Collective”  Evaluate unit under consideration using experience of the Collective  Improve discovery and evaluation methods based on weapon systems success Learning gives us our title “Collective Mind” Key Technology: Statistical Machine Learning

6 Example: Locomotive Selection The Mission: Select 12 Locomotives to go from CA to PA Design and Configuration Type Electrical System … Utilization Information Age Mileage Average miles/day … Maintenance Information Time elapsed since last repair Median time between repairs Median time from repair to next recommendation (Rx) … Decision Support Data: 200+ Basic Parameters Data from GE Transportation Locomotives Network Enabled

7 Identifying Peers f2: miles/day f1: Rx/Year Peers of Unit 5700 Unit 5700 f3: ?? P1 P2 P3 P4 P6 P5 P7 = Peers of Unit 5700 Collective: Peers with similarity measure Peer experience forms Mission Reliability (MR) rank Learning: similarity measure updated by accuracy of MR Rank

8 State of the Practice: non-Machine Learning Selection Criteria % of Correctly Classified Units: Top 20% (Sample Performance) Lowest Mileage 17% Newest Units 18% Random20% Highest Energy (MWHRS) generated 24% Highest Miles/ Hours Moving 26% Highest Percentage Hours Moving 29% Lowest Percentage of Failures in Most Critical Subsystem 38% Lowest Ratio: Recommendations / Age 49%

9 Accuracy and Robustness of the Peer Approach Excellent Performance with Existing Sensors on Legacy Systems 48.1% 55.8% 63.5% 32% 37% 20% 8% 6% 5.0% 15.0% 25.0% 35.0% 45.0% 55.0% 65.0% 75.0% 123 Time Slices Selection Performance Learned Peers non-Machine Learning Random (52 out of 262 units)(52 out of 634 units) (52 out of 845 units) Highest contributing parameters assumed missing Learned Peers show better performance & continuous improvement Robust to Missing Information

10 Guidance  Possible Futures Test in of Current Technology in Services  Proposals with AFRL to eLog21: F110 or F100  Partnership with AMCOM, Fort Rucker: Blackhawk Development of Technology  Meeting with ONR and Marines ALP Development of Vision  Meeting with DARPA IXO

11 Questions? Collective Mind Continuously Improving Equipment Maintenance Norman Sondheimer U of Massachusetts Amherst Amherst, MA 06001 sondheimer@som.umass.edu Al Wallace RPI Troy, NY 12180 wallaw@rpi.edu Peter Will USC/ISI Marina del Rey, CA will@isi.edu Piero Bonissone, Steve Linthicum, Anil Varma GE Global Research Schenectady, NY 12301 linthicu@crd.ge.com


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