Time Series Anomaly Detection Techniques for Predictive Maintenance

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.

Learn about:
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

Predictive Maintenance Toolbox Examples: https://bit.ly/41g4aKi

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.

Chapters:
00:00 Introduction to Anomaly Detection
01:03 Predictive Maintenance Basics
03:12 Types of Time Series Anomalies
04:20 Time Series Anomaly Detection Techniques
06:39 Data Exploration using Distance-Based Pattern Matching in MATLAB
13:37 AI Algorithm Development Workflow
15:03 Developing Anomaly Detection Algorithms in MATLAB
17:15 Feature Engineering with the Diagnostic Feature Designer
24:29 Training AI Models for Anomaly Detection
25:27 AI Models for Anomaly Detection: One-Class SVM
27:55 AI Models for Anomaly Detection: Isolation Forest
28:47 AI Models for Anomaly Detection: LSTM Autoencoder
34:44 Deploying Anomaly Detection Models
35:45 Further Resources

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