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Industrial Machinery Anomaly Detection

version 1.1.3 (69 MB) by Rachel Johnson
Train an autoencoder on normal operating data from an industrial machine to predict anomalies.

286 Downloads

Updated 30 Sep 2021

From GitHub

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Editor's Note: This file was selected as MATLAB Central Pick of the Week

Industrial Machinery Anomaly Detection

View <Industrial Machinery Anomaly Detection using an Autoencoder> on File Exchange

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.

Cite As

Rachel Johnson (2021). Industrial Machinery Anomaly Detection (https://github.com/matlab-deep-learning/Industrial-Machinery-Anomaly-Detection), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2021a
Compatible with R2020b and later releases
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.