Industrial Machinery Anomaly Detection
Updated 17 Jun 2021
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.
- Open the MATLAB Project
- 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.
Rachel Johnson (2021). Industrial Machinery Anomaly Detection (https://github.com/matlab-deep-learning/Industrial-Machinery-Anomaly-Detection), GitHub. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
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