Deep Learning for Fault Detection: Part 1 – Sound Analysis

Deep Learning for Fault Detection_ audio

1 hour | Free | Online | Arpad Forberger

In any industrial environment, the ability to detect and address malfunctions in machines before they cause any disruption to operations is critical. In recent years, there has been a significant shift in the way industries approach anomaly detection for machines, thanks to the emergence of cutting-edge technologies such as Artificial Intelligence (AI) and deep learning. These advanced technologies have revolutionized traditional anomaly detection methods and are now widely adopted across various industries.

Sound is a rich source of information when it comes to identifying machine faults. Irregular sounds, vibrations, and peculiar noises can be early indicators of underlying issues within machinery. With the help of deep learning, we can train machines to recognize these patterns and detect deviations that may suggest a fault.

Join this webinar to explore how engineers can detect the faults in a machine based on sound analysis using deep learning in MATLAB.

During this webinar, you will:

  • Obtain a fundamental understanding of how deep learning works in MATLAB and how it can be applied to sound analysis for fault detection.
  • Discover the process of creating and training deep-learning models specifically designed to identify machine faults based on sound data.
  • Explore how to deploy your deep learning model in real-world scenarios for continuous fault detection, ensuring minimal downtime and maintenance costs.

Who Should Attend

  • Engineers and professionals in the fields of industrial automation, manufacturing, and reliability engineering
  • Data scientists and machine learning enthusiasts interested in applying deep learning to fault detection
  • Anyone looking to gain insights into the latest technologies transforming the world of machine fault detection

Featured products

All products mentioned in this webinar are developed by MathWorks.

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About the Speaker(s)

  • Arpad Forberger

    Application Engineer

    Arpad is an application engineer at Gamax Laboratory Solutions. His research and consulting work focus on technical computing and finite element modeling.

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