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. 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:

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
2. Open MATLAB project Aircompressorclassification.prj
3. Open and run Part03_Deployment.mlx