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Innovative Wind Maintenance Management
CMMS
Innovative Wind Maintenance Management
CMMS
James Parle
Muir Data Systems
James Parle
Muir Data Systems
Road MapRoad Map
CMMS overview
Wind CMMS
Preliminary survey findings
Moving forward
General CMMSGeneral CMMS
Manufacturing
Fleet
Pharmaceutical
Facilities
Utilities
Example CMMS Savings without MobileExample CMMS Savings without Mobile
Company Benefit Savings
Improve production capacity (maintenance efficiency) 5 – 15%
Increase equipment efficiency 20%
Increase in production (preventative maintenance) 40%
Reduce maintenance budget 30%
Reduce production downtime 20%
Reduce maintenance labor costs (better scheduling) 10 – 30%
Reduction in overtime 10 – 50%
Labor productivity increase 30%
Inventory cost reduction 25%
Decrease maintenance material cost 20%
Source: industry benchmarks A.T. Kearny, Grant Thornton, PWC, Gartner, ARC, Tomkins, DOE
15 – 50%
O&M
Savings
Oil & Gas CMMSOil & Gas CMMS
Source: Siemens Oil & Gas
Hydro Asset Management Partnership
(HydroAMP)
Hydro Asset Management Partnership
(HydroAMP)
Evaluation Method
Data standard
Single database
Plant / utility reports
Open to all users
Expanding US Wind MarketExpanding US Wind Market
28% Growth
$50B End of
Warranty
30% Spent on
Maintenance
$6B O&M
2025
0
10,000
20,000
30,000
40,000
50,000
60,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
WindCapacity(MW)
Year
Current Solution Highly DatedCurrent Solution Highly Dated
Wind CMMS OverviewWind CMMS Overview
Why Wind CMMS?Why Wind CMMS?
Harsh
Environmental
Conditions
Remote Locations
Lots of Moving
Parts
Highly Distributed
Safety Concerns
Complete Maintenance Management SolutionComplete Maintenance Management Solution
Tablet
Management
Office
Cloud
Server
Analytics
Enterprise
Mgmt.
GridSCADA
Data
Blades TowerNacelleGeneratorGearbox
API
Maintenance Management Solution DetailsMaintenance Management Solution Details
CMMS Infrastructure / Database / Analysis
Data
Sharing
Data Source:
Tablet in the Field
Data Source:
Tablet in the Field
Data MovementData Movement
Data In-Take
Software
Modules
Data In-Take
Software
Modules
Large Scale Data AggregationLarge Scale Data Aggregation
Plant 1 Plant 2
Plant 1
Plant 3
Plant 2
Plant 2
Plant 4
Plant 3
Plant 1
Owner 1Owner 1
Owner 2Owner 2
Owner 3Owner 3
Predictive Maintenance
Anonymous Data
Aggregation System
Predictive Maintenance
Anonymous Data
Aggregation System
Predictive Maintenance ValuePredictive Maintenance Value
47% O&M Cost Reduction47% O&M Cost Reduction
Source: Electric Power Research Institute
Preliminary Survey FindingsPreliminary Survey Findings
Survey OverviewSurvey Overview
Field Data Office Storage Reporting Demographics
Technology · Time · SatisfactionTechnology · Time · Satisfaction
Company TypeCompany Type
Mainly ISPs & OOsMainly ISPs & OOs
Current PositionCurrent Position
More Engineers than ExpectedMore Engineers than Expected
Company SizeCompany Size
Highly Fragmented IndustryHighly Fragmented Industry
Work Orders per YearWork Orders per Year
Average 10 Work Orders per DayAverage 10 Work Orders per Day
Time in Current PositionTime in Current Position
60% Changed Job Title in Last 2 Years60% Changed Job Title in Last 2 Years
Work Order Process ChangesWork Order Process Changes
72% Change Annually or Less Often72% Change Annually or Less Often
Work Order AccuracyWork Order Accuracy
Not Good for AnalysisNot Good for Analysis
Work Order AnalysisWork Order Analysis
Limited Analysis PerformedLimited Analysis Performed
Work Order AnalysisWork Order Analysis
0
3
5
0.00
2.50
5.00
Field Office Storage Reporting
0
30
60
SatisfactionSatisfaction
TimeTime
TechnologyTechnology
Extremely
Moderately
Not at all
Analog
Digital
Somewhat
Digital
0 Mins
30 Mins
60 Mins
Satisfaction vs. Employee TypeSatisfaction vs. Employee Type
0.00
2.50
5.00
Field Office Storage Reporting
Technicians
Engineers
Managers
Executives
Moderately
Not at all
Extremely
Technology vs. Company TypeTechnology vs. Company Type
0
2.5
5
Field Office Storage Reporting
ISP
OO
OEM
Digital
Somewhat
Digital
Analog
CMMS Company PrioritiesCMMS Company Priorities
0
3
6
Reduced
inventory costs
Customer
satisfaction
Increased
uptime
Greater
accuracy
Predictive
analysis
Scheduling
ISP
OO
OEM
Aveage
Linear (Aveage)
Least
important
Somewhat
important
Most
important
Wind CMMS LandscapeWind CMMS Landscape
INDUSTRY
SPECIFIC
INDUSTRY
SPECIFIC
INDUSTRY
AGNOSTIC
INDUSTRY
AGNOSTIC
LOW
COST
LOW
COST
HIGH
COST
HIGH
COST
Generic Low CostGeneric Low Cost
Generic High CostGeneric High Cost In-House SolutionsIn-House Solutions
Wind Specific Low CostWind Specific Low Cost
Switching CostsSwitching Costs
Cultural Shift RequiredCultural Shift Required
Demand Better SolutionsDemand Better Solutions
CMMS reduces O&M costs
Technology is ready
Wind lagging behind other industries
Data becoming competitive advantage
Predictive maintenance is the prize
Offshore Wind CMMSOffshore Wind CMMS
james@muirdata.com

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22 WIPP Future Advancements and Operational Safety
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21 WIPP recovery and Operational Safety
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20 EPA Review of DOE’s 2014 Compliance Recertification Application for WIPP
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19 Repository designs in bedded salt, the KOSINA-Project
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18 Interaction between Operational Safety and Long-Term Safety (Project BASEL)
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17 Salt Reconsolidation
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16 Reconsolidation of granular salt (DAEF report)
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15 Outcome of the Repoperm Project
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14 Radiological Consequences Analysis for a HLW Repository in Bedded Salt in ...
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13 "New results of the KOSINA project - Generic geological models / Integrity...
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12 Salt testing: Low deviatoric stress data
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09 Invited Lecture: Salt Creep at Low Deviatoric Stress
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James Parle: 2013 Sandia Wind Plant Reliability Workshop

  • 1. Innovative Wind Maintenance Management CMMS Innovative Wind Maintenance Management CMMS James Parle Muir Data Systems James Parle Muir Data Systems
  • 2.
  • 3. Road MapRoad Map CMMS overview Wind CMMS Preliminary survey findings Moving forward
  • 5. Example CMMS Savings without MobileExample CMMS Savings without Mobile Company Benefit Savings Improve production capacity (maintenance efficiency) 5 – 15% Increase equipment efficiency 20% Increase in production (preventative maintenance) 40% Reduce maintenance budget 30% Reduce production downtime 20% Reduce maintenance labor costs (better scheduling) 10 – 30% Reduction in overtime 10 – 50% Labor productivity increase 30% Inventory cost reduction 25% Decrease maintenance material cost 20% Source: industry benchmarks A.T. Kearny, Grant Thornton, PWC, Gartner, ARC, Tomkins, DOE 15 – 50% O&M Savings
  • 6. Oil & Gas CMMSOil & Gas CMMS Source: Siemens Oil & Gas
  • 7. Hydro Asset Management Partnership (HydroAMP) Hydro Asset Management Partnership (HydroAMP) Evaluation Method Data standard Single database Plant / utility reports Open to all users
  • 8. Expanding US Wind MarketExpanding US Wind Market 28% Growth $50B End of Warranty 30% Spent on Maintenance $6B O&M 2025 0 10,000 20,000 30,000 40,000 50,000 60,000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 WindCapacity(MW) Year
  • 9. Current Solution Highly DatedCurrent Solution Highly Dated
  • 10. Wind CMMS OverviewWind CMMS Overview
  • 11. Why Wind CMMS?Why Wind CMMS? Harsh Environmental Conditions Remote Locations Lots of Moving Parts Highly Distributed Safety Concerns
  • 12. Complete Maintenance Management SolutionComplete Maintenance Management Solution Tablet Management Office Cloud Server Analytics
  • 13. Enterprise Mgmt. GridSCADA Data Blades TowerNacelleGeneratorGearbox API Maintenance Management Solution DetailsMaintenance Management Solution Details CMMS Infrastructure / Database / Analysis Data Sharing Data Source: Tablet in the Field Data Source: Tablet in the Field Data MovementData Movement Data In-Take Software Modules Data In-Take Software Modules
  • 14. Large Scale Data AggregationLarge Scale Data Aggregation Plant 1 Plant 2 Plant 1 Plant 3 Plant 2 Plant 2 Plant 4 Plant 3 Plant 1 Owner 1Owner 1 Owner 2Owner 2 Owner 3Owner 3 Predictive Maintenance Anonymous Data Aggregation System Predictive Maintenance Anonymous Data Aggregation System
  • 15. Predictive Maintenance ValuePredictive Maintenance Value 47% O&M Cost Reduction47% O&M Cost Reduction Source: Electric Power Research Institute
  • 17. Survey OverviewSurvey Overview Field Data Office Storage Reporting Demographics Technology · Time · SatisfactionTechnology · Time · Satisfaction
  • 18. Company TypeCompany Type Mainly ISPs & OOsMainly ISPs & OOs
  • 19. Current PositionCurrent Position More Engineers than ExpectedMore Engineers than Expected
  • 20. Company SizeCompany Size Highly Fragmented IndustryHighly Fragmented Industry
  • 21. Work Orders per YearWork Orders per Year Average 10 Work Orders per DayAverage 10 Work Orders per Day
  • 22. Time in Current PositionTime in Current Position 60% Changed Job Title in Last 2 Years60% Changed Job Title in Last 2 Years
  • 23. Work Order Process ChangesWork Order Process Changes 72% Change Annually or Less Often72% Change Annually or Less Often
  • 24. Work Order AccuracyWork Order Accuracy Not Good for AnalysisNot Good for Analysis
  • 25. Work Order AnalysisWork Order Analysis Limited Analysis PerformedLimited Analysis Performed
  • 26. Work Order AnalysisWork Order Analysis 0 3 5 0.00 2.50 5.00 Field Office Storage Reporting 0 30 60 SatisfactionSatisfaction TimeTime TechnologyTechnology Extremely Moderately Not at all Analog Digital Somewhat Digital 0 Mins 30 Mins 60 Mins
  • 27. Satisfaction vs. Employee TypeSatisfaction vs. Employee Type 0.00 2.50 5.00 Field Office Storage Reporting Technicians Engineers Managers Executives Moderately Not at all Extremely
  • 28. Technology vs. Company TypeTechnology vs. Company Type 0 2.5 5 Field Office Storage Reporting ISP OO OEM Digital Somewhat Digital Analog
  • 29. CMMS Company PrioritiesCMMS Company Priorities 0 3 6 Reduced inventory costs Customer satisfaction Increased uptime Greater accuracy Predictive analysis Scheduling ISP OO OEM Aveage Linear (Aveage) Least important Somewhat important Most important
  • 30. Wind CMMS LandscapeWind CMMS Landscape INDUSTRY SPECIFIC INDUSTRY SPECIFIC INDUSTRY AGNOSTIC INDUSTRY AGNOSTIC LOW COST LOW COST HIGH COST HIGH COST Generic Low CostGeneric Low Cost Generic High CostGeneric High Cost In-House SolutionsIn-House Solutions Wind Specific Low CostWind Specific Low Cost
  • 33. Demand Better SolutionsDemand Better Solutions CMMS reduces O&M costs Technology is ready Wind lagging behind other industries Data becoming competitive advantage Predictive maintenance is the prize