Signal processing of machining process data

Type of course:

Digital learning, Lesson

Language:

Duration:

20 minutes

Workload:

2 hours

Proficiency:

Beginner

Target:

Manager, Professionals

This lesson presents the main methods of data preprocessing, the step of the machine learning process between data acquisition and feature extraction. These methods include selection and separation of relevant data, data filtering and conversion to the frequency domain with Fourier transform. Some examples of these methods applied in the machining context will be shown.

Learning outcomes

The learner is able to describe the concepts of data separation and data filtering after completing this lesson.
The learner is able to list and describe various methods of data filtering and conversion to frequency domain along with use-cases for the application of these methods after completing this lesson.