Fault Detection Using 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.
We show examples on how to perform the following parts of the Deep Learning workflow:
Part1 - Data Preparation
Part2 - Modeling
Part3 - Deployment
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:
Open MATLAB project Aircompressorclassification.prj
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:
Open MATLAB project Aircompressorclassification.prj
Open and run Part02_Modeling.mlx
Part 3 - Deployment
This example shows how to generate optimized c++ code ready for deployment.
To run:
Open MATLAB project Aircompressorclassification.prj
Open and run Part03_Deployment.mlx
Citar como
David Willingham (2024). Fault Detection Using Deep Learning Classification (https://github.com/matlab-deep-learning/Fault-Detection-Using-Deep-Learning-Classification), GitHub. Recuperado .
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