AFTERNOON WORKSHOP: Tuesday 11 October 2016 

13:00 - 17:00

Advancements In Condition Monitoring Across A Whole Fleet

A deep dive into current best practice condition monitoring and performance analysis for fleets of wind farms, with practical examples, cutting edge research and group discussion

Operator Perspective

Whole Of Fleet Monitoring

Richard will give a short overview of how we try to handle the massive data wave and share some ideas as to how the process could be more efficient in the future. Afterwards, data problems and their solutions in using data analytics on an automatic basis will be covered. Then the room will be left open to discussion with Q+A. 

Richard Distl , Managing Director, ImWind Operations GmbH  


Presentation And Group Discussion 

Tools And Techniques To Enable Accurate Predictive Maintenance  

This session will identify which components and subcomponents can be reliably monitored in a predictive capacity with the available technology. It will also address how you can develop the capability for predictive maintenance across your fleet and which tools have been proven to be reliable. There will also be a sharing of experiences, within the group and from the presenters, about lessons learnt from implementing a predictive maintenance approach.    


Afternoon Refreshments & Networking Opportunity  


New Developments In Condition Monitoring

You will get a preview of condition monitoring tools and techniques on the horizon, and the chance to question their developers to determine how they might improve the performance of your wind fleet in the future.

Taking An Integrated Condition Monitoring Approach For A Fleet Of Turbines

  • How to combine data from 10min SCADA, 1sec SCADA, CMS (vibrations), maintenance and meteo at the fleet level
  • Machine learning techniques for modelling of SCADA data focussed on fault detection
  • Advanced vibration signal processing: what is the added value? 

Prof. Dr Jan Helsen , Coordinator Big Data Analytics, OWI-lab

Analysis Of Wind Turbine SCADA Data Using Artificial Intelligence Techniques To Detect Faults

Prof. Simon Watson, Professor Of Wind Energy, Loughborough University

Active Ultrasonic Sensors for Measuring Wind Turbine Bearing Performance

A pioneering alternate method for monitoring wind turbine bearings using active (rather than the usually monitored passive) acoustic signals. Find out about how this new technology works and whether it could help you to more accurately catch failures before they occur.

Prof. Rob S Dwyer-Joyce
, Director Of The Leonardo Centre For Tribology, University Of Sheffield

Reducing False Alarm Rates Across A Fleet Of Wind Farms

False alarms are often a headache for condition monitoring systems’ operators. In the Wind industry, common practices for defining a threshold violation do not always apply, as variations in wind speed can cause differences in vibration levels far exceeding the changes caused by an early fault. Find out how this situation can be greatly improved with analysing only the ‘proper’ data, when the variability is acceptable.

Prof. Tomasz Barszcz, 
Vice President Of Polish Society Of Technical Diagnostics,  AGH University Of Science And Technology


Close of Workshop