https://github.com/matlab-deep-learning/fault-detection-using-deep-learning-classification

This demo shows how to prepare, model, and deploy a deep learning LSTM based classification algorithm to identify the condition or output of a mechanical air compressor.
https://github.com/matlab-deep-learning/fault-detection-using-deep-learning-classification

deep-learning example fault-detection lstm matlab matlab-deep-learning

Last synced: 3 months ago
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This demo shows how to prepare, model, and deploy a deep learning LSTM based classification algorithm to identify the condition or output of a mechanical air compressor.

• Host: GitHub
• URL: https://github.com/matlab-deep-learning/fault-detection-using-deep-learning-classification
• Owner: matlab-deep-learning
• License: other
• Created: 2020-03-19T19:56:02.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-09-06T19:43:28.000Z (over 2 years ago)
• Last Synced: 2024-01-28T03:40:37.458Z (about 1 year ago)
• Topics: deep-learning, example, fault-detection, lstm, matlab, matlab-deep-learning
• Language: C++
• Homepage: https://www.mathworks.com/products/deep-learning.html
• Size: 49.3 MB
• Stars: 64
• Watchers: 6
• Forks: 27
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Fault Detection Using LSTM Deep Learning Classification

This demo shows the full deep learning workflow for an example of signal data. We show how to prepare, model, and deploy a deep learning LSTM based classification algorithm to identify the condition or output of a mechanical air compressor.

![](Images/bearingsignal.png)

![](Images/confusionmat.png)

We show examples on how to perform the following parts of the Deep Learning workflow:

- Part1 - Data Preparation

- Part2 - Modeling

- Part3 - Deployment

![](Images/deeplearningworkflow.png)

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. There is also a significant data copy required the first time you run the project.

## Part 1 - Data Preparation

This example shows how to extract the set of acoustic features that will be used as inputs to the LSTM Deep Learning network.

To run:

1. Open MATLAB project Aircompressorclassification.prj

2. Open and run Part01_DataPreparation.mlx

## Part 2 - Modeling

This example shows how to train LSTM network to classify multiple modes of operation that include healthy and unhealthy signals.

To run:

1. Open MATLAB project Aircompressorclassification.prj

2. Open and run Part02_Modeling.mlx

## Part 3 - Deployment

This example shows how to generate optimized c++ code ready for deployment. 

To run:

1. Open MATLAB project Aircompressorclassification.prj

1. Open MATLAB project Aircompressorclassification.prj

2. Open and run Part03_Deployment.mlx