Industrial Machinery Anomaly Detection

Version 1.1.3 (69 Mo) par Rachel Johnson
Train an autoencoder on normal operating data from an industrial machine to predict anomalies.
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Mise à jour 30 sept. 2021
Afficher la licence sur GitHub

Note de l’éditeur : This file was selected as MATLAB Central Pick of the Week

Industrial Machinery Anomaly Detection

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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:

  1. Open the MATLAB Project AnomalyDetection.prj
  2. Open Parts 1-3 on the Project Shortcuts tab

MathWorks® Products (http://www.mathworks.com)

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.

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Citation pour cette source

Rachel Johnson (2024). Industrial Machinery Anomaly Detection (https://github.com/matlab-deep-learning/Industrial-Machinery-Anomaly-Detection), GitHub. Extrait(e) le .

Compatibilité avec les versions de MATLAB
Créé avec R2021a
Compatible avec les versions R2020b et ultérieures
Plateformes compatibles
Windows macOS Linux

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Les versions qui utilisent la branche GitHub par défaut ne peuvent pas être téléchargées

Version Publié le Notes de version
1.1.3

Renaming
1.1.2

Updated links
1.1.1

Renaming and minor edits
1.1

Improved visualizations and explanations
1.0.1

GitHub repository now located on matlab-deep-learning
1.0.0

Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.
Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.