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    Windelin Project and Smart Sensor presentation

    Smart sensor at the edge presentation. Technology and approach being used by Nexedi during Windelin EU R&D project.
    • Last Update:2018-11-10
    • Version:001
    • Language:en

    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

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