Windelin Project and Smart Sensor

Windelin R & D project

  • Who am I?
  • EU funded project (2.5M EUR), 2015 - 2018
  • Participants: Nexedi, MariaDB and Micromega Dynamics (Woelfel group)
  • Goal: develop a big data solution on top of Wendelin platform for wind turbines management and smart sensor at the edge utilizing GPU and machine learning technology

Smart sensor at the edge architecture

  • Placed inside wind turbine, based on Nvidia TX2 board
  • Close to real time computation of an anomaly index and action (shutdown turbine, alarm)
  • No need of network connectivity or human interventions
  • How: Machine Learning for failure prediction and anomaly detection using GPU
  • ML Model built server side, same model runs inside smart sensor - i.e. developed once, used anywhere -> less code and maintenance


Machine learning simplified

  • Data = usually a set of numbers representing a machine state (wind turbine's state)
  • Model = "formula" for converting with minimal losses input data to output data where
    • Model created by iterating over and over (hundreds of times) over TBs of data using powerful GPU cards (server side)
    • Model = very small file,  quickly executable on either GPU or CPU  into any embedded system
  • Anomaly = how well input data "fits" into model, the less if fits the higher its anomaly score is which mean "ALARM" state

Industry application for smart sensors

  • Industry agnostic approach. Data source abstraction.
  • Generic sensor which can interface with any device in industry supporting TCP / IP protocol
  • Machine learning  - no more black box and magic but hard-stone mathematics constantly being improved
  • Machine learning library agnostic approach. Use what you need: tensorflow, pytorch or scikit-learn both at the edge (sensor) or server side (backend)

Thank You

Image Nexedi Office
  • Nexedi SA
  • 147 Rue du Ballon
  • 59110 La Madeleine
  • France