This Predictive Maintenance example trains a deep learning autoencoder on normal operating data from an industrial machine. The example walks through:

• Extracting relevant features from industrial vibration timeseries data using the Diagnostic Feature Designer app
• Setting up and training an LSTM-based autoencoder to detect abnormal behavior
• Evaluating the results on a validation dataset

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. Run Part 1 - Data Preparation & Feature Extraction
3. Run Part 2 - Modeling

Requires MATLAB® release R2020b or newer and:

• Predictive Maintenance Toolbox™
• Deep Learning Toolbox™

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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