Feature Engineering for Fault Detection using Wavelet Scattering
In this webinar, you will learn how the MathWorks team used iterative feature engineering techniques to design a fault detection application for predictive maintenance. Using a hydraulic rock drill dataset of time series data, we will explore feature engineering for classifying a variety of fault types. We will start by assessing a broad set of statistical features, and then apply wavelet scattering to increase the accuracy of our prediction based on techniques from literature. Finally, we will refine the feature set using a combination of unsupervised techniques and well-established feature ranking methods.
This webinar will be relevant to anyone interested in designing accurate fault detection and predictive maintenance algorithms from time series sensor data.
- Accessing and organizing data
- Iterative feature engineering from general purpose statistical features through wavelet scattering.
- Unsupervised clustering techniques for feature selection
- Training classification models using low-code apps
- Deploying algorithms to the edge and cloud
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenters
Russell Graves: Russell is an Application Engineer at MathWorks focused on machine learning and systems engineering. Prior to joining MathWorks, Russell worked with the University of Tennessee and Oak Ridge National Laboratory in intelligent transportation systems research with a focus on multi-agent machine learning and complex systems controls. Russell holds a B.S. and M.S. in Mechanical Engineering from The University of Tennessee.
José Montoya Bedoya: José is an Application Engineer at MathWorks focused on supporting Latin America. Prior Joining MathWorks, José worked as an Application Engineer with MathWorks distributor in Colombia, supporting companies in Energy Production, Finance, and Education, including applications in AI, Data Analysis, and Control Systems. José holds a B.S. and M.S in Electronics Engineering from Universidad Pontificia Bolivariana, Colombia.
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