Signal Preprocessing and Cleaning with MATLAB
Overview
In this session, you’ll learn how to use MATLAB apps to clean anomalies in sensor data and improve the accuracy of modal analysis. We will compare results using raw, noisy data versus prepocessed signals highlighting how preprocessing leads to more reliable frequency estimates.
Agenda
Today’s session focuses on an aircraft wing experiment:
- Review sensor placement and examine errors introduced during data collection.
- Assess signal errors using the Signal Analyzer app.
- Build a workflow to clean anomalies and prepare signals for analysis.
Highlights
- Perform signal analysis and exploration with Signal Analyzer—no coding required.
- Use built-in and custom functions to preprocess signals.
- Automatically generate MATLAB functions from your preprocessing workflow for repeatability.
Who Should Attend
Any STEM professional interested in an app-based approach to condition signals for further analysis.
About the Presenter
Ayon Dey, PhD | Senior Applications Engineer, Energy Resources, MathWorks
Ayon is a Senior Application Engineer at MathWorks based in Texas. Ayon’s experience spans over 30 years working in multiple signal-data analysis roles for companies in the energy industry based in the USA, Canada, South Africa, Saudi Arabia, and the UAE, as well as for research institutions in Canada and The Netherlands. Ayon holds a PhD degree in Applied Physics from Delft University of Technology (Netherlands), an MSc degree in Geology and Geophysics from University of Calgary (Canada), and a BSc in Applied Mathematics from Memorial University of Newfoundland (Canada).
