Industrial Machinery Anomaly Detection
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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:
- Open the MATLAB Project
AnomalyDetection.prj
- Open Parts 1-3 on the Project Shortcuts tab
http://www.mathworks.com)
MathWorks® Products (Requires MATLAB® release R2021b or newer and:
License
The license for Industrial Machinery Anomaly Detection using an Autoencoder is available in the license.txt file in this GitHub repository.
Community Support
Copyright 2021 The MathWorks, Inc.
Zitieren als
Rachel Johnson (2024). Industrial Machinery Anomaly Detection (https://github.com/matlab-deep-learning/Industrial-Machinery-Anomaly-Detection), GitHub. Abgerufen .
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Versionen, die den GitHub-Standardzweig verwenden, können nicht heruntergeladen werden
Version | Veröffentlicht | Versionshinweise | |
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1.1.3 | Renaming |
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1.1.2 | Updated links |
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1.1.1 | Renaming and minor edits |
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1.1 | Improved visualizations and explanations |
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1.0.1 | GitHub repository now located on matlab-deep-learning |
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1.0.0 |
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