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Anomaly Detection for Industrial Processes and Machinery with MATLAB

Overview

Many industries are looking to AI to deliver increased efficiency and improve product quality by automating production process monitoring and maintenance scheduling. Even when production lines are instrumented with sensors as part of digital transformation, engineering teams often lack the specialized skills required by predictive maintenance and advanced process analytics. This webinar will demonstrate statistical and machine learning techniques in MATLAB on real-world datasets to monitor manufacturing processes and detect equipment anomalies. 

Highlights

  • Preprocessing sensor data
  • Identifying condition indicators
  • Using deep learning and machine learning for anomaly detection algorithms
  • Operationalizing algorithms on embedded systems and IT/OT systems

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenter

Timothy Kyung is an Application Engineer at MathWorks supporting the Government and Defense Industry with technical expertise in application deployment, interfacing with third party software, and parallelization. He holds a B.S. and M.S. in Mechanical Engineering with a focus in robotics from Carnegie Mellon University. 

Product Focus

Anomaly Detection for Industrial Processes and Machinery with MATLAB

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