Smart Machine Phase II  

Business Case:

Today, valuable information lies buried inside individual machine controls on the shop floor. Extracting information from these isolated islands is difficult and time consuming.  Lean staffed, multi-shift operations make it difficult for operating personnel to maintain an accurate situational awareness from one shift to the next.  Maintenance personnel receive scant advance warning and are called to diagnose faults based on a filtered verbal explanation of the problem. 

As a result, operating, support, and management personnel are left to make decisions based on incomplete and subjective information.  Problems and inefficiencies remain hidden. Opportunities for continuous improvement are not realized because the supporting facts are not known.

This smart machine project will equip different selected machines at project partners’ sites with the capability to automatically gather and report their performance in a consistent fashion.  The cost/benefit ratio to gather and compile this information is radically changed for the better.

Approach:

• Begin Phase II by first documenting the new project partners’ selected machines’ existing interfaces to production execution, process engineering, and maintenance support functions.  Perform the same tasks for expanded pilots at existing sites.
• Implement proof-of-concept “Smart Machine” capabilities and infrastructure for these machines, focused on the maintenance support functions. 
• Gather data and experience at each site during an initial usage period. 
• Develop strategies and enhancements for these machines’ connections to the maintenance support function. 
• Demonstrate selected enhancements; gathering additional data and experience at the sites during a subsequent usage period. 
• Deliver report final results in 2006.  This will include definition of specific, achievable objectives for subsequent project phases and/or follow-on Next Generation Depot and Smart Machine Platform Initiatives.

Phase II will increase the knowledge base by targeting different types of equipment and by driving toward new uses of collected data.

Technologies: 

• Experiments will be conducted on selected legacy equipment from different manufacturers incorporating smart machine sensor and data logging technology.  Based on the capabilities of individual machines, this could include data reporting from an existing sensor such as the Spindle Probe, or new sensors such as G-Meter, Oil Sentinel, Pressure Sensors, Thermocouples, etc.
• Automatically log the machine sensor and performance data to a FREEDOM E-LOG database as a part of normal production over an extended time period.  Allow this database to be interviewed by operating, support, and management personnel using open-source industry standards.
• Demonstrate autonomous data-mining processes that interrogate the database and report pertinent and timely information to people and other software processes, specifically Computerized Maintenance Management Systems (CMMS), using open-source industry standards.
• Apply IMS Center automated pattern recognition and Bayesian Belief Network methodologies to make maintenance inferences using the data reported by the machines during production.
• Demonstrate examples of secure and appropriate automated data sharing both within a company/site and with external suppliers.
• Develop proof-of-concept interfaces and functions that focus on maintenance support.

Anticipated Participants:

• Cincinnati Lamb – project champion and primary technology provider
• Caterpillar – technology user and pilot site provider
• ATS – Caterpillar’s maintenance service provider
• Vought Aircraft – technology user and pilot site provider
• Sikorsky Aircraft – technology user and pilot site provider
• Red River Army Depot – depot partner and pilot site provider
• Cherry Point NADEP – depot partner
• WR-ALC – depot partner and pilot site provider
• Pearl Harbor NSY – depot partner and pilot site provider
• NIST – partnered through CRADA with Cincinnati Lamb

NCMS Contact: Tony Haynes, (734) 995-4930, tonyh@ncms.org