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Intelligent Malfunction Prognostics From equipment condition monitoring to optimal asset management EWEA Annual Conference, Brussels, Belgium, March 14-17,

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Presentation on theme: "Intelligent Malfunction Prognostics From equipment condition monitoring to optimal asset management EWEA Annual Conference, Brussels, Belgium, March 14-17,"— Presentation transcript:

1 Intelligent Malfunction Prognostics From equipment condition monitoring to optimal asset management EWEA Annual Conference, Brussels, Belgium, March 14-17, 2011

2 Copyright © 2011 by Cassantec Ltd. 1 There are many CMS for WT on the market, differing in their functional scope, WT component focus, learning capabilities and life cycle stage Solution Profile Scope Monitoring (Predictive) Diagnostics Prognostics Learning Manual Automated, unit-level Automated, fleet-wide Stage R&D Validation 100sInstallation 10s 1000s Focus Pitch Gearbox ConverterDrive Generator Yaw Intelligent Malfunction Prognostics

3 Copyright © 2011 by Cassantec Ltd. 2 Intelligent malfunction prognostics can be provided through reliability reports, supporting critical decisions on maintenance scope and schedule a bc d e f g h Reliability Report a View condition diagnostics b View malfunction diagnostics c View malfunction prognostics d Aggregate prognostics e Cross-check maintenance plan f Extend condition data sources g Extend malfunction modes h Extend prognostic horizon Intelligent Malfunction Prognostics

4 Copyright © 2011 by Cassantec Ltd. 3 Monitor power output and technical condition of each unit in the fleet Review diagnostic insights for units in marginal or critical condition Use prognostic foresight to optimize fleet maintenance process Identify and avoid unnecessary preventive measures and costs Anticipate malfunctions before failure, damage, foregone output Realize a commercially optimal fleet maintenance schedule Unit View Reliability reports aggregate to a fleet level, with navigation functions, consolidating diagnostic insight and prognostic foresight for several units Unit vs. Fleet View Fleet View Intelligent Malfunction Prognostics

5 Copyright © 2011 by Cassantec Ltd. 4 Crucial condition data is captured through vibration and lubricant sensors, and directly uploaded into the WT controller via standard protocols Nacelle Rotor hub Slow rotating shaft Fast rotating shaft Rotor bearing Bearing Tower Generator Gearbox Blade Pitch WT Server Foundation R2 R1 E1 SCADA controller Sensor controller T,R,E Ethernet Switch T2 T1 T4 T5 T8 T6 T7 Wind Turbine Yaw drive V,L V1 V2 V4 V5 V8 V7 V6 Hardware Package V1 L1 Very low frequency accelerometer High sensitivity & accuracy Latest-generation technology Armored integral cable V,L Versatile Profibus terminal Easy plug-in installation Straightforward configuration Meeting OEM standards Inline twin laser particle counter Latest-generation technology Integrated humidity sensor Stainless steel block V8 … Intelligent Malfunction Prognostics T3 V3 L1

6 Copyright © 2011 by Cassantec Ltd. 5 Hosting Network Mgt and PLC Controller Consolidation of SCADA data from all WT Consolidation of additional sensor data Forwarding of consolidated data batches to Cassantec Wind Park Gather malfunction and failure statistics Inform suppliers of components affected Improve quality of WT components affected Ascertain state-of- the-art prognostic solution Report Review Maintenance & Service Scheduling Report Review Asset Mgt. Decisions Spare Part Mgt. Capacity Forecasts Further Wind Park Data Mgt & Archiving Condition Monitoring Malfunction Diagnostics Failure Prognostics Intelligent Reporting Reliability reports are updated with new condition and process data in periodical intervals, and delivered to the operators on-line via reliability portal Etc. A LAN / Ethernet or similar B Router, Firewall Internet Cassantec Server WT Manufacturer WP Operator WP Insurer Download batches of condition and process data (V,L,T,R,E) for all WT in regular intervals Upload WP Reliability Reports in corresponding intervals WP Service Providers ISDN, ADSL, or similar Further Wind Park Etc. WP Server WT Server Fleet Server WP = Wind Park, WT = Wind Turbine Intelligent Malfunction Prognostics Data Transfer

7 Copyright © 2011 by Cassantec Ltd. 6 We have calibrated and validated our reliability reporting solution with off-line and on-line data from several wind farms predominantly in the U.S. Intelligent Malfunction Prognostics Example Wind Farm: Buffalo Ridge near Alta, IA, U.S.A. WF Capacity:150 x 750 kW = MW WT Models:Zond Z-46 (now GE) Sampling period:2006 – 2010 (on- & off-line) Sampling intervals:continuous to 6 months Malfunction modes:e.g. Gearbox LS wheel wear Causes:e.g. Micro pitting, contributed by water ingress Impact:e.g. Bearing life reduces by factor 3 Learnings: Upgrade sensor hardware Monitor condition dynamics Exploit fleet intelligence Field Validation of Solution Illustrative Map source: Logo source:

8 Copyright © 2011 by Cassantec Ltd. 7 We achieve malfunction and failure prognostics over an explicit time horizon exceeding the best predictive diagnostic approaches on the market so far Prognostic Horizon Prognostic horizon [Days after last update] Value added by reliability report $ $$ $$$ Our current capability Potential future capability Equipment Procurement & Replacement Work Order Scheduling Maintenance Cycle Scheduling Unscheduled Outage Coordination Routine Monitoring Competitor capabilities Intelligent Malfunction Prognostics

9 Copyright © 2011 by Cassantec Ltd. 8 Benefits of reliability reports have been confirmed by operators beyond wind power – these benefits increase over time through machine learning Prognostic Accuracy In retrospect, 99% of predictable malfunctions were accurately predicted, with a horizon of up to 5 years (!) Operator knowledge was exceeded by 20%, with several surprises (e.g. cartridge sealing) Diagnostics und prognostics are enhanced over time through machine learning June 2010July 2010March 2009August 2008 April 2007 Cartridge seals Mech. seals Cartridge seals Coupling Alignment Coupling Alignment Mech. seal Nov. OK Intelligent Malfunction Prognostics

10 Copyright © 2011 by Cassantec Ltd. 9 Example for learning value bands Machine Learning Value bands must be continuously learned from the empirical condition data: Even with constant equipment utilization, value bands may shift over time! Collective learning process for equipment of same type (flagging before adjusting) Intelligent Malfunction Prognostics Normal value bands shift and evolve Static value bands not useful

11 Copyright © 2011 by Cassantec Ltd. 10 This learning process is initialized at different parameter value levels – gearbox oil has fluctuating initial levels of cleanliness, mostly within tolerance intervals Gearbox oil is rarely clean to begin with: units start up with varying levels of initial contami- nation Learning Process Initialization Learning process initialization for equipment of same type Intelligent Malfunction Prognostics Example for flexible initialization

12 Copyright © 2011 by Cassantec Ltd. 11 In summary, we are targeting new features allowing commercially optimal fleet maintenance schedules, cutting costs of failure, damage and lost power output State-of-the-art sensor hardware High-end specialized sensors for wind power applications Integration of latest technologies (e.g. twin laser particle counters) Full utilization (and no duplication) of existing data and infrastructure (SCADA) Intelligent diagnostics Comprehensive expertise on model-specific malfunction and failure sources and risk Automated learning from ongoing monitoring of the entire fleet Reference data from other WT, fleets, applications Advanced prognostics Extended prognostic horizon through computational stochastic model Full utilization of recorded and archived condition and process data histories per WT Prognostic accuracy exceeding capabilities of any competing product on the market Cost-effective advice on optimal fleet asset management Reduction of risk and costs for WT malfunction, failure, damage and foregone power output Reduction of risk and costs of unnecessary preventive measures and foregone power output Realization of a commercially optimal condition-based fleet maintenance schedule State-of- the-art Enhanced New Technical & Commercial Target Benefits Intelligent Malfunction Prognostics

13 Copyright © 2011 by Cassantec Ltd. 12 Further Information For further information, please review our brochure on-line, and contact us by or telephone Cassantec team behind this presentation Frank Kirschnick Zurich, Switzerland Heinz Giovanelli Munich & Zurich Gary Ellis Cleveland, Ohio Shuang Yuan Zurich, Switzerland Mart Grasmeder Cleveland, Ohio Katerina Stamou Zurich, Switzerland Mila Vodovozova Zurich, Switzerland To obtain more information, please download our brochure at Or send an to Intelligent Malfunction Prognostics

14 Copyright © 2011 by Cassantec Ltd. 13 Intelligent Malfunction Prognostics Appendix

15 Copyright © 2011 by Cassantec Ltd. 14 Cassantec is an independent provider of integrated, automated prognostic services for critical power plant equipment with a unique, protected technology Meaning:Cassantec = Cassandra Technologies Position:Independent provider of integrated, automated equipment condition diagnostics and malfunction prognostics Technology:Novel combination of best practice techniques from Operations Research, Artificial Intelligence and Data Mining Comprehensive condition data reference base (since 1993): 500k data sets of 20 equipment types, 2000 models Offering: Online Condition Monitoring Systems and Reliability Reports on a subscription basis for equipment operators worldwide References:Chemical and Power industries (U.S.A. and Europe) including nuclear and fossil-fired power plants and wind farms Promoters:Power corporations, private investors, Swiss government (CTI) Industry Partner: Leading independent U.S. lubricant lab (Insight Services) Academic Partner:EPFL, ETHZ, Stanford University Cassandra prophet of critical future events in the Greek mythology Profile of Cassantec Ltd. Intelligent Malfunction Prognostics

16 Copyright © 2011 by Cassantec Ltd. 15 Our prognostic services have been successfully applied to a wide range of power equipment, with operators in different regions and industry segments Cassantec References (Excerpt) WindFossilNuclearChemicalSteel Intelligent Malfunction Prognostics


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