Industrial Machinery Anomaly Detection

Train an autoencoder on normal operating data from an industrial machine to predict anomalies.
Updated 30 Sep 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


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 (

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.

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Cite As

Rachel Johnson (2024). 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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Version Published Release Notes



Updated links


Renaming and minor edits


Improved visualizations and explanations


GitHub repository now located on matlab-deep-learning


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.