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:
- Open the MATLAB Project
AnomalyDetection.prj
- Run Part 1 - Data Preparation & Feature Extraction
- Run Part 2 - Modeling
MathWorks® Products (http://www.mathworks.com)
Requires MATLAB® release R2020b or newer and:
The license for Industrial Machinery Anomaly Detection using an Autoencoder is available in the license.txt file in this GitHub repository.
Copyright 2021 The MathWorks, Inc.