Industrial Machinery Anomaly Detection

Train an autoencoder on normal operating data from an industrial machine to predict anomalies.
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Aggiornato 30 set 2021

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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.

Community Support

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Copyright 2021 The MathWorks, Inc.

Cita come

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

Compatibilità della release di MATLAB
Creato con R2021a
Compatibile con R2020b e release successive
Compatibilità della piattaforma
Windows macOS Linux

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Le versioni che utilizzano il ramo predefinito di GitHub non possono essere scaricate

Versione Pubblicato Note della release
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

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.