Optimizing processes involves reducing downtime caused by equipment breakdowns. Instead of reacting to machine failures or performing unnecessary maintenance, it is more effective to predict when a machine is likely to break down. Predictive Maintenance is now a widely adopted practice in the industry.
Join us for a hands-on workshop session where we will demonstrate the workflows offered by MATLAB and Simulink. From data-driven, AI-based solutions to technical and rigorous physics-based simulations, you will gain valuable insights and practical knowledge.
This workshop is suitable for anyone who are interested in learning about the workflow for developing Predictive Maintenance (PdM) Algorithms with MATLAB and Simulink.
Highlights:
In this hands-on workshop, you will be able to learn:
Generating data from Simulink
Data pre-processing and feature extraction
Implementing feature extraction
Predictive modeling
List of Toolboxes:
Installation Guidelines for the Workshop
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Jonathon Tee
Application Engineer
Jonathan Tee, an Application Engineer at TechSource Systems, holds a Master of Engineering degree in Electrical and Electronics from Nottingham Malaysia. With expertise in RF and microwave technology, he has designed Phased Arrays and tackled Signal and Power Integrity challenges.
Jonathan's passion extends to using MATLAB for Communication Systems and RF Propagation. In his spare time, he's a 3D modeling enthusiast and has even built his own 3D printer from scratch. His innovative approach and technical prowess make him a standout professional in the field.
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