Time Series Anomaly Detection Techniques for Predictive Maintenance

Start Time	End Time
22 Oct 2024, 9:00 AM EDT	22 Oct 2024, 10:00 AM EDT
22 Oct 2024, 2:00 PM EDT	22 Oct 2024, 3:00 PM EDT

Overview

Fault data is critical when designing predictive maintenance algorithms but is often difficult to obtain and organize. Many organizations are faced with a growing sea of time series sensor data, most of which represents normal operation. How can engineers analyze this data and design anomaly detection algorithms to identify potential problems in industrial equipment? Using real-world examples, this webinar will introduce you to a variety of statistical and AI-based anomaly detection techniques for time series data.

Highlights

Highlights include:

Organizing, analyzing, and preprocessing time series sensor data
Feature engineering using Diagnostic Feature Designer
Distance-based approaches for exploring anomalies in historical data
One-class machine learning and deep learning approaches for algorithm development
Comparing and testing algorithm performance
Deploying anomaly detection algorithms in a streaming environment

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

James Wiken is a Senior Application Engineer at MathWorks, where he helps people with all things MATLAB, with a particular emphasis on Test & Measurement, Application Development, and Software Development Workflows. James also holds an S.B. and S.M. degree in Aerospace Engineering from MIT, where he specialized in controls and autonomous flight.

Product Focus

Time Series Anomaly Detection Techniques for Predictive Maintenance

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22 Oct 2024, 9:00 AM EDT

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