Renewables and O&M Cost Reduction
Ed Wagner Co-chair, IIC, Energy Committee VP, Sentient Science
Introduce Yourself
• Name • Company • Business challenge to solve • What’s your Industrial Internet strategy for O&M?
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DEPARTMENT OF DEFENSE
DEPARTMENT OF ENERGY
NATIONAL SCIENCE FOUNDATION
Our 10 Year Research Pedigree Invited a New way to Measure and Test Rotating Equipment Computationally
• Scalability and Timing • Commercialization • Substitutability • Material Input Requirements • Intermittency • Energy Density • Water • The Law of Receding Horizons • Energy Return on Investment
The Challenge for Renewables
Using the Industrial Internet to Move From Planned Maintenance to Predictive Health Maintenance March 11, 2015 4
The Challenge for Wind O&M
• How do I reduce my exposure for GBX failures?
• How to I balance spending for both immediate GBX availability and long term fleet value?
• How do I get the best pricing on GBX replacement inventory?
• How do I make sure I won’t break the bank – spending above OPEX ceilings?
• How do I engage financial managers when budgeting uses historic O&M data instead of future projection?
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Intelligent Machines – Advanced Analytics – People at Work
Applying a New Generation of Prognostics Across the Industrial Internet
• Physical Layer
• Applica=ons Layer
• People/Process Layer
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Wind Case Study – Large Vertically- Integrated Provider
• Offers the lowest energy pricing through ver=cal oriented efficiencies
• 600+ wind turbines, one major wind-‐turbine brand plus assorted others (acquisi=ons), star=ng to see increasing number of gearbox failures
• Majority of fleet is off-‐warranty • CBM mix in place • Strong management pressure (and bonus) on maintaining or
reducing ANNUAL OPEX spend
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• What’s the needed planning horizon?
• Diagnostics and prognostics • Planned preventative
maintenance (PPM) costs versus predictive health maintenance (PHM)
PPM vs. PHM
Past
DIAGONSTICS
Future
PROGNOSTICS
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Crack Nuclea,on and Propaga,on
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Time
Con
diti
on
First visible detection (vibration, borescope, heat, etc.) Detection
Life Extension Decision Support
Failure/Crack Progression
Non-‐Visible Non-‐Visible Non-‐Visible Visible
How Does Prognostics Work on Gearboxes? Predict Earlier & More Accurately
Not Visible Not Visible Not Visible Visible
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“As Is”
Quantify Impact of: Lubrication change, auto-lubrication systems, partial de-rating, etc.
Prognostics allows you to quantify “what-if” scenarios to extend RUL.
“To Be”
Years Un)l Gearbox Failure Years Un)l Gearbox Failure
Percen
tage of Fleet Failure
Percen
tage of Fleet Failure
Bearing Performance Linked to GBX Failure Rates
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How we make a Multi-Physics Prognostics Model
1) DigitalClone System™ Loads, Requirements & System Life
2) DigitalClone Material™ Characterize & Create Microstructure Model
3) DigitalClone Component™ Fric=on, Wear & Lubrica=on Surface Treatments
5) DigitalClone Component™ Predict Component Failure Mode/Failure Life
4) DigitalClone Component™ Simulate Stress in Microstructure to Predict Crack Ini=a=on & Propaga=on
6) DigitalClone Live™ Output Model: Predict-‐Acquire-‐Confirm-‐Control
UI Example
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List of assets worst to best
UI Example
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List of sub components worst to best
$2,000,000.00
$7,000,000.00
$12,000,000.00
$17,000,000.00
$22,000,000.00
$27,000,000.00
$32,000,000.00
$37,000,000.00
$42,000,000.00
$47,000,000.00
$52,000,000.00
$-
$5.00
$10.00
$15.00
$20.00
$25.00
$30.00
6 7 8 9 10 11 12 13 14 15 16
AS-IS MW/Hr Cost TO-BE MW/Hr Cost AS-IS Yearly Cost
• Variable cost for gearbox replacement
• In most cases unplanned and unbudgeted cost for gearbox and main bearings
• As high as $27 per MWhr in some cases
Industry Average – 1.5mw WTG – 500 Units
MW/Hr Cost
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Post Warrantee Failures
Industry Average – 1.5mw WTG – 500 Units
$2,000,000.00
$7,000,000.00
$12,000,000.00
$17,000,000.00
$22,000,000.00
$27,000,000.00
$32,000,000.00
$37,000,000.00
$42,000,000.00
$47,000,000.00
$52,000,000.00
$-
$5.00
$10.00
$15.00
$20.00
$25.00
$30.00
6 7 8 9 10 11 12 13 14 15 16
AS-IS MW/Hr Cost TO-BE MW/Hr Cost AS-IS Yearly Cost TO-BE Yearly Cost
MW/Hr Cost
• Through life extension, failures rates can be managed and reduced
• Uptower component replacement
• Up/de-rate • Bearing replacements • Oil additives
• Provide a minimum 25% improvement each year to the failure rates
5 years, $19M savings, 500% ROI
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O&M Optimization (cut O&M 5% - Revenue increase 4%) Using Sentient DigitalClone models • Inspections • Budgets – monthly to 10 year • Optimized duty cycle • Oil & bearing management • Maintenance schedules
Inventory and Supply Chain Optimization (Build data at lower cost and time) • Big data analytics • Maximo • SAP
Fleet Process Optimization: ($10K reduction per asset) • CBM-PHM –Industrial Internet
investment Cap-ex reduction • Sensor reduction • Decision data at lowest cost • People optimization • Asset acquisition support • M&A support • Refinancing upport
O&M Aftermarket Life Extension –& Super Components Decisions (O&M reductions of 10% - revenue increase 6%) OEM/Supplier SaaS Test & Compare: • Upgrades • Repair, reman, replace • Contract negotiation
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Applying Prognostics for Inventory and Supply Chain Across the Industrial Internet
Services to operate and Services to deliver
SMART GRID
ASTRO Platform Example
Control your product's lifecycle through ROI trade-‐off studies monitoring, simula,on, and
op,miza,on.
Confirm prognos=c accuracy through advanced processing, alerts, and repor=ng tools.
Acquire opera=onal data, using your CBM system or Sen=ent Science’s advanced sensor suite, to tailor service for each individual asset.
Predict the performance and health of your GBX assets,
confidently and quickly, both today and into the future.
How to Serialize the Prognostic Model for Distributed Assets
Ac)on Cost
1 Develop a PHM-‐powered list of GBX and components to replace over the next 3 years, where and when.
$150,000
2 De-‐rate 40 units and do oil changes to extend their life and get them to a planned up-‐tower replacement schedule.
$1,000,000
3 Borescope those 40 machines to confirm prognos=cs. $100,000
4 Do 20 up-‐tower component replacements. $1,000,000
5 Re-‐rate the upgraded gearboxes. $200,000
6 Do 8 gearbox replacements with carburized bearings. $2,000,000
7 Develop upgraded energy plan to op=mize energy output against extending fleet life beyond 20 years.
$25,000
8 Cover headcount and opera=on. $500,000
9 Go to island for vaca=on. $25,000
ALL IN $5,000,000
Case Study: I have $5 Million to spend on Gearbox Maintenance this year. How should I spend it?
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Ac)on Cost
1 Develop a PHM-‐powered list of GBX and components to replace over the next 3 years, where and when.
$150,000
2 De-‐rate 40 units and do oil changes to extend their life and get them to a planned up-‐tower replacement schedule.
$1,000,000
3 Borescope those 40 machines to confirm prognos=cs. $100,000
4 Do 20 up-‐tower component replacements. $1,000,000
5 Re-‐rate the upgraded gearboxes. $200,000
6 Do 8 gearbox replacements with carburized bearings. $2,000,000
7 Develop upgraded energy plan to op=mize energy output against extending fleet life beyond 20 years.
$25,000
8 Cover headcount and opera=on. $500,000
9 Go to island for vaca=on. $25,000
ALL IN $5,000,000
Case Study: I have $5 Million to spend on Gearbox Maintenance this year. How should I spend it?
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Go to an Island for Vacation
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1. Its now possible to plan for up-‐tower component repairs instead of full gearbox replacement with a mul=-‐year plan of where, when, and how many components should be replaced. ü Drama)c reduc)on in the cost of full GBX replacements
2. Its now possible to plan gearbox replacement, inventory, and supply chain planning with a 10-‐year horizon. ü BeHer pricing and inventory availability on supplier contracts
3. Its now possible to maintain a fleet using prognos=cs (going forward planning) instead of PPM and diagnos=cs only input. ü BeHer, more reliable mul)-‐year budget planning
4. Prognos=cs can help extend the RUL of components and gearboxes, provide beler input planning, and reduce your O&M budget. ü BeHer balance between short term repair spend and long term asset value
What Can We Learn Here?
Outcomes
1. Customer 1: Failure rate reduced by +50% – Customer budgeted and planned or 20+ gearboxes replacements for 2014 – Through up-tower component replacements, derating, etc. actual gearbox replacements reduced to <10
gearboxes – Through individual turbine trade-off reports, this customer increased ROI through an asset specific
optimization plan
2. Customer 2: Identified and confirmed 8 failed gearboxes not in their top 20 imminent failure watch list
– 90% correlation with borescope conducted on top 20 gearboxes for exact component failed – Previous top 20 list based on analytic and CMS based identification
3. Customer 3: No historical data or failures due to young fleet, Sentient correctly predicted next three gearbox failures
– No historical data to drive analytics based detection – Customer was able to plan and prepare for these gearbox failures in 2014
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Examples
Worlds Most Tested Products
Products with the Lowest Cost of Operation
What’s Your Industrial Internet Strategy?
• Predictive maintenance saving up to 12 percent over scheduled repairs, reducing overall maintenance costs up to 30 percent, and eliminating breakdowns up to 70 percent
• Oil and gas exploration and production predicting onshore and offshore oil pump failures to help minimize lost production
• Using data science to direct a set of performance dials and levers (speed, torque, pitch, yaw, aerodynamics and turbine controls) to fine-tune a wind turbine’s operation and gain up to 5 percent energy output
• Your Turn
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