MTConnect Challenge
For
Machine Monitoring & Big Data
Prepared by B. Blomquist
763-263-5306
Bill.blomquist@remmele.com
May - 2013
Machine Monitoring & Big Data
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1.0 Abstract
The objective of this paper is to demonstrate the capability of MTConnect and Big Data analysis to
conduct shop floor machine monitoring and analysis, accurately and inexpensively. The availability
of collecting machine data via the MTConnect protocol, visual data acquired by cameras and from
multiple business systems will enable accurate analysis of machine utilization and shop floor
efficiency. Current shop floor machine data collection struggles with finding out why the machine is
not running from a plethora of diverse reasons. The conflict resides in requiring the machine
operator to manually input the reason for why the machine is not running. When the data used for
analysis is dependent of manual input, the resulting report is compromised and cannot be trusted for
production decisions. Shop managers want to know the foremost reason why the machine is not
running and implement a fix. An application which executes unobtrusively and reports accurate
analysis is of high value.
2.0
Application Goal
Build an unobtrusive application, which provides accurate reports of why the machine is/was not
running, obtained by analyzing a mash-up of data from multiple points.
The application will require no interaction with the machine operator.
The application will determine who is present at what times at the machine. (Operators, managers
and maintenance personnel)
The application will collect actual near real time machine data from the machine control using
MTConnect, business systems and from multiple low cost vision cameras which will be stationed at
various points around the machine. The application will utilize ‘Big Data’ analytics to determine the
reason the machine was/is not running and why it is/was running. The application will create a
holistic report of what took place at the machine for any given time period, or manufacture of a part.
The application will also provide reports that reflect if a specific reason for why the machine is not
running is trending better or worse.
Machine Monitoring & Big Data
3.0
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Technical Requirements
The application will require that the machine control is provisioned with an MTConnect adapter.
Also all accessory sensors, like spindle vibration signal, oil chiller temperature, tooling RFID chip
etc. will provide data to the application via the MTConnect protocol.
Other optional inputs of data may be obtained from:
 Materials Resource Planning (MRP)system
 Manufacturing Execution Systems (MES)system
 Product Lifetime Management (PLM)system
 Engineering Resource Planning (ERP)system
 Product Data Management (PDM)system
 Emails
 Phone calls
 Personnel RFID badges
The more data which can be collected, the more accurate the ‘Big Data’ analytic engine will be able
to mash-up the data and determine the reason for downtime or uptime.
Visual data will be obtained by cameras monitoring key points of the machine/device
 Spindle or work effector (currently used at RTI Remmele Inc)
 Operator control interface area
 Pallet shuttle or work queue area
 Cutting Tool loading area
 Chip conveyor or waste removal area
 Machine/device, maintenance access panel doors
 Operator work area
Other locations of visual data collection:
 Shipping and Receiving
 Inspection department work area
 Coordinate Measuring Machine
 Tool crib area
All the technical requirements needed to realize this application can be met at this time.
Visual data will need to be analyzed with vision algorithms
There are multiple existing visual data analytical engines.
(ref. Sight Machine)
A secure cloud server infrastructure will be needed to store very large amounts of scalable data.
There are multiple existing cloud infrastructures
Amazon cloud services
Microsoft Azure services
The ‘Big Data’ backend engine will execute on a cloud based infrastructure to maximize processing
power scalability.
There are existing examples of ‘Big Data’ analytical applications in use.
(ref. IBM Big data development platform)
Machine Monitoring & Big Data
4.0
Benefits
The application will provide multiple benefits:
 Accurate reasons for machine/device downtime
 Accurate reasons for machine /device uptime
 Unobtrusive- No required production personnel input required
 Ability to see if process improvement is working or not
 Ability to analyze manufacturing problems in a detailed holistic view
 Ability to prove actual part process mapping (receipt of process)
 Ability to provide process simulation software ‘current state’ of process
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