Industrial Machinery Anomaly Detection

This example applies various anomaly detection approaches to operating data from an industrial machine. Specifically it covers:

• Extracting relevant features from industrial vibration timeseries data using the Diagnostic Feature Designer app
• Anomaly detection using several statistical, machine learning, and deep learning techniques, including:
  • LSTM-based autoencoders
  • One-class SVM
  • Isolation forest
  • Robust covariance and Mahalanobis distance

Setup

This demo is implemented as a MATLAB® project and will require you to open the project to run it. The project will manage all paths and shortcuts you need.

To Run:

1. Open the MATLAB Project AnomalyDetection.prj
2. Open Parts 1-3 on the Project Shortcuts tab

MathWorks® Products (http://www.mathworks.com)

Requires MATLAB® release R2021b or newer and:

• Predictive Maintenance Toolbox™
• Deep Learning Toolbox™
• Statistics and Machine Learning Toolbox™

License

The license for Industrial Machinery Anomaly Detection using an Autoencoder is available in the license.txt file in this GitHub repository.

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