Fault Detection Using LSTM Deep Learning Classification
14 Jan 2021 | 15:00-16:00 (GMT+8)
Online via Webex


About The Event

Deep learning offers new opportunities to develop predictive models to solve a wide variety of signal processing applications. MATLAB® supports the entire workflow—from exploration to implementation of signal processing systems built on deep networks.

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:

  • Part 1 - Data Preparation: Extract the set of acoustic features that will be used as inputs to the LSTM Deep Learning network.

  • Part 2 - Modelling: Train LSTM network to classify multiple modes of operation that include healthy and unhealthy signals.

  • Part 3 - Deployment: Generate optimized c++ code ready for deployment.


The Speaker

Siti Safwana
Siti Safwana
Application Engineer

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