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2016 smrp 101616
Alan M Ross
Manufacturing Process Reliability
Reliability Centered Maintenance
for Critical Electrical Equipment
Jason Dennison
Track 2
Alan Ross
Vice President of Reliability, SDMyers Inc.
• BS Mechanical Engineering from
Georgia Institute of Technology
• MBA from Georgia State University
• CEO and Founder of Corporate
Development Institute
• CEO and Founder of Kingdom Companies
• Vice President of Reliability at SDMyers
Jason C. Dennison
Sr. Solutions Manager, On Now Digital
• BS Chemical Engineering w/Polymer
Specialization, University of Akron
• Six Sigma Black Belt
• 13 years PM and technical leadership experience
• DGA Monitor Technology study (2015) co-author
• Adjunct instructor for Principles of Transformer
Maintenance
Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed.
Industrialization Run to failure (RTF)
How did we get here?
(and where is here?)
Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed.
The greatest generation took control
The war to
end all wars
(WWII)
Mechanization:
Maintenance Focus
Complexity of Production
Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed.
The boomers take control
Boom times:
70s, 80s, 90s
Just In Time
Lean Manufacturing
More regulations (OSHA, EPA)
Reliability takes root
The New
Millennium
Globalization
Maximum Capacity
Reliability-Centered Maint.
ISO 55000 Asset Mgmnt.
Preparing for the NextGen
who will face new challenges with new tools and approaches
REDUCE
ISO 55000
It will change the way we manage transformers
TRANSFER
ACCEPT AVOIDrisk
Priority 2
Priority 1
ISO 55000
It will change the way we manage transformers
REDUCE
AVOID
risk
management
ISO 5500 will create the process
to classify transformers from RTF to Mission-Critical
Corporate/
Organization
Management
Manage Asset
Portfolio
Manage Asset
Systems Networks
Manage individual Assets
over their Life Cycles
© 2014 The Woodhouse Partnership
Mission-Critical
Transformers
System-Critical
Transformers
Reliability and
Maintenance
Activities efficiency
and effectiveness
System performance,
cost and risk control
Portfolio investment
performance and
Keeping stakeholders happy
Unplanned outages
will have even greater consequences
on organizational mission.
What matters most is not the cost of the
transformer but the total cost of failure.
The best approach to structuring an organization
in order to avoid or reduce failures is determined
by these 3 key factors:
Competent
Personnel1 • Subject Matter Experts
• Teaming Skills
• Common Focus
Data
Centricity2 • CMMS/EMS Standardization
• System Integrity
• Essentially Predictive
Matrix
Capability3 • Mature Departmental Integration
• Flexibility
• Dynamic Decision Making
A Robust Risk Assessment Program
Three Essential Elements
The Data Element
One source of truth
The Human Element
Competency and consistency
The Process Element
Simplification and standardization
Transformer Risk
A reliability-centered approach to maintenance
What does it power?
o Can it be financially quantified
in real dollars?
o Can it be categorized
(on a scale of 1 to 4)?
o Is it mission-critical?
o Is it system-critical?
Transformer Life Cycle Reliability
The age of a transformer must be evaluated
with the condition and load as significant
contributing factors.
Age
Condition
Load
Life Cycle
The Total Cost of Catastrophic Failure
Safety and environmental impact will continue to
play greater roles in life cycle asset management.
DESTRUCTIVE
CONSEQUENCES
o Environment
o Safety
o Company Brand
Disclaimer
“Past performance is not an indicator
of future returns” may not apply.
Nameplate Historical Data Analytics can and
must assist us in making predictive decision on
transformer reliability
The case of the Colonel Carbon Monoxide in the
parlor with an axe…
Prioritize Risk
This is the next step to take after determining
criticality for a class or group of assets.
Mathematical Formula: Risk Priority Number (RPN)
RPN = Occurrence x Severity x Detection
Process Formula:
Risk = Occurrence x Severity
Detection and Maintenance
The level on the Reliability-Centered
Maintenance scale should be based on
criticality and risk rather than
your past practices.
New Manufacturing Risk
Transformer Failure Rates
The Reliability Professional Dilemma
o Data history
o New data acquisition
o Standardization of
process
o Data analytics
and then we add
online monitors
The Generational Challenge
The boomer brain drain will be replaced with
next-generation Google-brained staff who are
largely unprepared to meet all of the challenges.
The trend toward transformer monitoring
is the result of a shift from testing, repair
and maintenance to corporate
asset reliability.
Taking monitoring seriously
The standard
monitoring package
for new transformers
at a large municipality
in the Southern US
features 8 continuous
monitoring devices.
How many monitors are we talking about?
Main Tank
Temperature
Pressure Relief
Rapid Pressure Rise Relay
Liquid Level
Smart Breather
3rd Party Smart Sensors
Windings
Fiber Optic Winding Temp
Simulated Winding Temp
Partial Discharge (PD)
Dissolved Gas Analysis (DGA)
Geomagnetically Induced Current
(GIC)
Bushings
Capacitance Current Value
Capacitance Alarm Set Points
Tan Delta Value
Tan Delta Alarm Set Points
Temperature Value
Temperature Alarm Set Point
Leakage Current Value + Set Points
Load Tap Changer
Contact Wear Status
Current Position + Range
Tap Run Time + Count
Motor Current Power
Motor Actuation Counter
Alarm Set Points
LTC Temperature and Differentials
Cooling Monitoring
Motor Current Power + Motor Run
Time
Alarm Set Points
Cooling Bank Temperature +
Differentials
Flow Indicator Status
Efficiency Status
Conservator Tank
Liquid Level
Buccholz Relay
Bladder Rupture Alarm
Source: Gauge and
Monitor Supplier Example:
33 Monitoring Options
Monitor X
Detection
Technology
Oil
DGA Gases
IoT
DGA Monitoring
Principles of Operation
The New Threat
The impact of the Internet of things (IoT) will ultimately
be one of great benefit, but the sheer magnitude and
complexity of data will create data chaos.
By 2020, the IoT will have expanded at a much faster
rate, resulting in a population of about 26 billion units
(“things”) at that time. – GARTNER
The National Cable and
Telecommunications
Association puts the
2020 estimate at more
than 50 billion units.
https://www.ncta.com/platform/industry-news/infographic-the-growth-of-the-internet-of-things/
Samsung’s Connected Refrigerator
• Shared calendar
• News
• Social
• Streaming music
• 3 Cameras to view inside
Who asked for this?
The Internet of Things
The useful IoT is about:
• Self-directed controls
• Finding exceptions to patterns
(DGA Monitors, Smart Meters)
• Developing new patterns (Fleet
Movement, Smart Grid)
• Autonomous controls
• Enforcing and alerting continuous
patterns (HVAC, Security, Medical)
We need to start concerning ourselves with the
Useful Internet of Things
• “85% of the billions of sensors
and data access points needed
to monitor machines exist today,
but aren’t being accessed.”
– IMS RESEARCH
• “Less than 1% of Internet of Things
data is currently used, mostly for
alarms and real-time control.”
– MCKINSEY GLOBAL INSTITUTE
IoT Data Usage
DGA monitoring challenges
• Data Chaos (see graphic)
• Access to the data
(non-networked, networked,
regulatory concerns)
• Limited mostly to oil
• Quality of info (false alarms)
• Price / Capital
The benefits are great,
but there are monitoring challenges
With odds of 9 in 100,000: You are only slightly less likely to be struck by lightning
(1/12,000) in your lifetime than to find the real alarm data on your own (1/11,111).
The least-interesting pie chart in the deck
Sample of 48 DGA Monitors
in 24 months
- 100,000 records
- 479 total alarms
- 31 valid alarms:
Communications
- 9 valid alarms:
DGA of consequence
Thing /
Data Source
Internal Analytics /
Immediate Feedback
Data Relationships /
Data Warehouse
Diagnostic / Reporting
Analytics
Could / Database
Azure, AWS, SQL, PI etc.
UIoT Analytics: Principles of Operation
Data Analysis and Analytics
• Time boundaries are a challenge: < 1s, seconds, minutes, hours,
days, months, years are all intervals of data gathering to reconcile
in data warehouse for summary
• Network connectivity and communication and IT support between
controls networking, LAN, WAN, cloud
• Business process between maintenance, reliability, technical, IT,
corporate policy, local – state – federal policy
• Cyber-security for critical asset data
• Things will cry wolf
• Administrative overhead for setup and maintenance
• Technical overhead for data analysis and reporting
Data Management and Analytics Challenges
The Engineer of the Future
must be trained and developed for a new tomorrow
alan.ross@sdmyers.com
jason.dennison@onnowdigital.com
Questions?
Thank you!

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2016 smrp 101616

  • 2. Alan M Ross Manufacturing Process Reliability Reliability Centered Maintenance for Critical Electrical Equipment Jason Dennison Track 2
  • 3. Alan Ross Vice President of Reliability, SDMyers Inc. • BS Mechanical Engineering from Georgia Institute of Technology • MBA from Georgia State University • CEO and Founder of Corporate Development Institute • CEO and Founder of Kingdom Companies • Vice President of Reliability at SDMyers
  • 4. Jason C. Dennison Sr. Solutions Manager, On Now Digital • BS Chemical Engineering w/Polymer Specialization, University of Akron • Six Sigma Black Belt • 13 years PM and technical leadership experience • DGA Monitor Technology study (2015) co-author • Adjunct instructor for Principles of Transformer Maintenance
  • 5. Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed. Industrialization Run to failure (RTF) How did we get here? (and where is here?)
  • 6. Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed. The greatest generation took control The war to end all wars (WWII) Mechanization: Maintenance Focus Complexity of Production
  • 7. Thanks to: John S. Moubray, Reliability-centered Maintenance 2nd Ed. The boomers take control Boom times: 70s, 80s, 90s Just In Time Lean Manufacturing More regulations (OSHA, EPA) Reliability takes root
  • 8. The New Millennium Globalization Maximum Capacity Reliability-Centered Maint. ISO 55000 Asset Mgmnt. Preparing for the NextGen who will face new challenges with new tools and approaches
  • 9. REDUCE ISO 55000 It will change the way we manage transformers TRANSFER ACCEPT AVOIDrisk Priority 2 Priority 1
  • 10. ISO 55000 It will change the way we manage transformers REDUCE AVOID risk management
  • 11. ISO 5500 will create the process to classify transformers from RTF to Mission-Critical Corporate/ Organization Management Manage Asset Portfolio Manage Asset Systems Networks Manage individual Assets over their Life Cycles © 2014 The Woodhouse Partnership Mission-Critical Transformers System-Critical Transformers Reliability and Maintenance Activities efficiency and effectiveness System performance, cost and risk control Portfolio investment performance and Keeping stakeholders happy
  • 12. Unplanned outages will have even greater consequences on organizational mission. What matters most is not the cost of the transformer but the total cost of failure.
  • 13. The best approach to structuring an organization in order to avoid or reduce failures is determined by these 3 key factors: Competent Personnel1 • Subject Matter Experts • Teaming Skills • Common Focus Data Centricity2 • CMMS/EMS Standardization • System Integrity • Essentially Predictive Matrix Capability3 • Mature Departmental Integration • Flexibility • Dynamic Decision Making
  • 14. A Robust Risk Assessment Program Three Essential Elements The Data Element One source of truth The Human Element Competency and consistency The Process Element Simplification and standardization
  • 15. Transformer Risk A reliability-centered approach to maintenance What does it power? o Can it be financially quantified in real dollars? o Can it be categorized (on a scale of 1 to 4)? o Is it mission-critical? o Is it system-critical?
  • 16. Transformer Life Cycle Reliability The age of a transformer must be evaluated with the condition and load as significant contributing factors. Age Condition Load Life Cycle
  • 17. The Total Cost of Catastrophic Failure Safety and environmental impact will continue to play greater roles in life cycle asset management. DESTRUCTIVE CONSEQUENCES o Environment o Safety o Company Brand
  • 18. Disclaimer “Past performance is not an indicator of future returns” may not apply. Nameplate Historical Data Analytics can and must assist us in making predictive decision on transformer reliability The case of the Colonel Carbon Monoxide in the parlor with an axe…
  • 19. Prioritize Risk This is the next step to take after determining criticality for a class or group of assets. Mathematical Formula: Risk Priority Number (RPN) RPN = Occurrence x Severity x Detection Process Formula: Risk = Occurrence x Severity Detection and Maintenance
  • 20. The level on the Reliability-Centered Maintenance scale should be based on criticality and risk rather than your past practices.
  • 22. The Reliability Professional Dilemma o Data history o New data acquisition o Standardization of process o Data analytics and then we add online monitors
  • 23. The Generational Challenge The boomer brain drain will be replaced with next-generation Google-brained staff who are largely unprepared to meet all of the challenges.
  • 24. The trend toward transformer monitoring is the result of a shift from testing, repair and maintenance to corporate asset reliability.
  • 25. Taking monitoring seriously The standard monitoring package for new transformers at a large municipality in the Southern US features 8 continuous monitoring devices.
  • 26. How many monitors are we talking about? Main Tank Temperature Pressure Relief Rapid Pressure Rise Relay Liquid Level Smart Breather 3rd Party Smart Sensors Windings Fiber Optic Winding Temp Simulated Winding Temp Partial Discharge (PD) Dissolved Gas Analysis (DGA) Geomagnetically Induced Current (GIC) Bushings Capacitance Current Value Capacitance Alarm Set Points Tan Delta Value Tan Delta Alarm Set Points Temperature Value Temperature Alarm Set Point Leakage Current Value + Set Points Load Tap Changer Contact Wear Status Current Position + Range Tap Run Time + Count Motor Current Power Motor Actuation Counter Alarm Set Points LTC Temperature and Differentials Cooling Monitoring Motor Current Power + Motor Run Time Alarm Set Points Cooling Bank Temperature + Differentials Flow Indicator Status Efficiency Status Conservator Tank Liquid Level Buccholz Relay Bladder Rupture Alarm Source: Gauge and Monitor Supplier Example: 33 Monitoring Options
  • 27. Monitor X Detection Technology Oil DGA Gases IoT DGA Monitoring Principles of Operation
  • 28. The New Threat The impact of the Internet of things (IoT) will ultimately be one of great benefit, but the sheer magnitude and complexity of data will create data chaos.
  • 29. By 2020, the IoT will have expanded at a much faster rate, resulting in a population of about 26 billion units (“things”) at that time. – GARTNER The National Cable and Telecommunications Association puts the 2020 estimate at more than 50 billion units. https://www.ncta.com/platform/industry-news/infographic-the-growth-of-the-internet-of-things/
  • 30. Samsung’s Connected Refrigerator • Shared calendar • News • Social • Streaming music • 3 Cameras to view inside Who asked for this? The Internet of Things
  • 31. The useful IoT is about: • Self-directed controls • Finding exceptions to patterns (DGA Monitors, Smart Meters) • Developing new patterns (Fleet Movement, Smart Grid) • Autonomous controls • Enforcing and alerting continuous patterns (HVAC, Security, Medical) We need to start concerning ourselves with the Useful Internet of Things
  • 32. • “85% of the billions of sensors and data access points needed to monitor machines exist today, but aren’t being accessed.” – IMS RESEARCH • “Less than 1% of Internet of Things data is currently used, mostly for alarms and real-time control.” – MCKINSEY GLOBAL INSTITUTE IoT Data Usage
  • 33. DGA monitoring challenges • Data Chaos (see graphic) • Access to the data (non-networked, networked, regulatory concerns) • Limited mostly to oil • Quality of info (false alarms) • Price / Capital The benefits are great, but there are monitoring challenges
  • 34. With odds of 9 in 100,000: You are only slightly less likely to be struck by lightning (1/12,000) in your lifetime than to find the real alarm data on your own (1/11,111). The least-interesting pie chart in the deck Sample of 48 DGA Monitors in 24 months - 100,000 records - 479 total alarms - 31 valid alarms: Communications - 9 valid alarms: DGA of consequence
  • 35. Thing / Data Source Internal Analytics / Immediate Feedback Data Relationships / Data Warehouse Diagnostic / Reporting Analytics Could / Database Azure, AWS, SQL, PI etc. UIoT Analytics: Principles of Operation
  • 36. Data Analysis and Analytics
  • 37. • Time boundaries are a challenge: < 1s, seconds, minutes, hours, days, months, years are all intervals of data gathering to reconcile in data warehouse for summary • Network connectivity and communication and IT support between controls networking, LAN, WAN, cloud • Business process between maintenance, reliability, technical, IT, corporate policy, local – state – federal policy • Cyber-security for critical asset data • Things will cry wolf • Administrative overhead for setup and maintenance • Technical overhead for data analysis and reporting Data Management and Analytics Challenges
  • 38. The Engineer of the Future must be trained and developed for a new tomorrow

Editor's Notes

  1. This trend towards monitoring as part of reliability – here’s one way it’s manifested itself. This represents the standard monitoring package for new transformers for a large municipality in the South This is *today* for a real power provider. Talk to gauge and monitor manufacturers and they’ll blow your mind with options…
  2. The trend continues – This is the number of monitoring devices one leading gauge and monitor provides to its customers. This is in the realm of what is commonly called the Internet of Things (IoT) and the Industrial Internet of Things (IIoT).
  3. More and more “things” (monitors and the like) are becoming available. Also, it isn’t clear if either of these numbers include Industrial IoT or if these are just Consumer IoT (sometimes called Human IoT / HIoT)
  4. IoT is a very, very broad term – here’s one example. Just an opportunity for a chuckle – not all of those 26 – 50+ billion things are going to be useful. But there are absolutely useful things for IoT and IIoT (Industrial Internet of Things)! Another example: connected egg tray will tell you how many eggs you have… that’s serious dedication to egg purchasing process.
  5. By the way, that fridge *could* be useful someday. What if the fridge knew when you put items in and could estimate when it’s no longer healthy to eat? It could potentially do this using embedded data in RFID on food packaging, near field communication (NFC) as food is put in the fridge, a network connection, a database of shelf life, and embedded data on items – could this one day save someone from food poisoning? Weird, but this is an example of autonomous control to enforce a continuous pattern of eating food that is safe to eat. OR, we could all continue to use common sense – this is a pretty expensive way to see if the milk has gone bad when you can just read a date on the packaging and take a sniff 
  6. All this break-through technology is amazing, but it only makes a difference if do something with the data being generated. Clearly there is a gap between the design/desire (useful application of data gathered) and practicality (most people don’t know what to do with all that data).
  7. Okay so there’s the big picture in what’s going on in the world - let’s bring it back a bit to power / transformers w/ examples of challenges in DGA Monitoring. It’s not free, but what if you could take DGA Monitor data, periodic laboratory data, visual inspection data, failure rate data of similar equipment, operational data like % load, and environmental data to analyze a transformer? These data elements all have common features (called a “keys”) in that they can be directly attributed to a piece of equipment, which means it’s completely feasible to compare and analyze any of this data.
  8. Odds of being struck by lightning in your life: 0.0000833 Odds of stumbling on real alarm: 0.000090
  9. That’s a real set of screenshots taking multiple tables of data from databases and web activity that were pulled together to summarize key points about service activities for SDMyers. Who’s going to look through the databases for info? Nobody. Who’s going to look at graphs that answer specific questions about key business metrics and data? Anyone who cares about maintaining a sustainable business. This report will can be updated with the most recent available data with the single click of a button to get up-to-the-minute summary.