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American Institute of Aeronautics and Astronautics

    Predictive Maintenance with MATLAB: A Data-Based Approach

    9 August, 2018. 1300-1430 Eastern Standard Time
    Member Price: $49 USD
    Non-Member Price: $99 USD
    Student Member Price: $9.99 USD

    Register for Replay Here


    Companies that make industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future.  However, using this data to build accurate and robust models that can be used for prediction requires a rare combination of equipment expertise and statistical know-how. 

    In this 90-minute webinar we will use machine learning techniques in MATLAB to estimate remaining useful life of equipment while highlighting some newer features and toolboxes. Using data from a real-world example, we will explore importing, pre-processing, and labeling data, as well as selecting features, and training and comparing multiple machine learning models.  We will show how MATLAB is used to build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.

    Attendees will be able to have all their MATLAB questions answered at the end of the presentation. 


    Learning Objectives

    By watching this session attendees will learn how to:

    • Investigate equipment health based on sensor data
    • Build a prognostics algorithm to forecast the remaining useful life of equipment
    • Deploy predictive maintenance algorithms to edge devices and IT centers


    Who Should Attend

    This webinar is intended for practicing engineers, designers, testers, technology managers, and graduate students interested in learning how MATLAB can be used in predictive maintenance settings.



    Adam Filion, Senior Product Manager at MathWorks
    Adam is a Senior Product Manager for Data Analytics at MathWorks. He has been at MathWorks for eight years focusing on using technologies such as machine learning, deep learning and big data within engineering application areas including predictive maintenance. Prior to MathWorks he was at Virginia Tech where he earned his M.S in Aerospace Engineering with a focus on nonlinear controls and periodic orbits in the restricted three-body problem.

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