Industrial Machinery Anomaly Detection
Updated 30 Sep 2021
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
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
- Open Parts 1-3 on the Project Shortcuts tab
MathWorks® Products (http://www.mathworks.com)
Requires MATLAB® release R2021b 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 (2022). 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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