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Predictive Maintenance Using Deep Learning

 Predictive maintenance allows equipment operators and manufacturers to assess the condition of machines, diagnose faults, and estimate time to failure. Because machines are increasingly complex and generate large amounts of data, many engineers are exploring deep learning approaches to achieve the best predictive results.

In this talk, you will discover how to use deep learning for:

-Anomaly detection of industrial equipment using vibration data

-Condition monitoring of an air compressor using an audio-based fault classifier

You’ll also see demonstrations of:

-Data Preparation: Generating features using Predictive Maintenance Toolbox™ and extracting features automatically from audio signals using Audio Toolbox™

-Modeling: Training audio and time-series deep learning models using Deep Learning Toolbox™"


0:00 Identifying Faults in Audio Data

1:43 Predictive Maintenance Key Takeaways

3:46 Predictive Maintenance Algorithm Development Workflow

4:56 Audio Fault Detection with Deep Learning using an LSTM

12:14: Vibration Anomaly Detection with Deep Learning using an Autoencoder

20:12 Project Results and Predictive Maintenance Success Stories

21:30 Key Takeaways

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