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

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