Modern engineering revolves around data: vibrations, pressure, sound, temperature or sensor outputs that continuously generate information. But how do you turn this raw data into real insight? This training teaches engineers to analyse signals, filter noise and detect anomalies using Fourier analysis and Python. You will learn how data reveals the underlying behaviour of machines and processes, and how to apply that knowledge to improve reliability and efficiency.

The goal of this training is to teach engineers how to interpret and analyse complex signals using mathematical methods such as Fourier, Laplace and wavelet transformations. You will learn to translate theory into practice through industrial examples and hands-on Python exercises.
After completion, you will be able to convert raw sensor data into concrete insights for maintenance, quality monitoring and process optimisation.

Fourier analysis enables engineers to see what is hidden inside a signal. Instead of reacting to failures, you will learn to predict where and when they occur. The method is applied in many sectors: analysing motor vibrations to detect imbalance, studying pressure signals to identify cavitation, measuring harmonics in power systems, or filtering sensor noise in medical devices.
From offshore systems to wind turbines and laboratories, this approach gives engineers a deeper understanding of the data behind their designs.

This programme is designed for engineers, data analysts and R&D specialists with a higher technical education (bachelor’s or master’s level) who work with measurement data, vibration signals or sensor systems. It is particularly relevant for professionals in mechanical engineering, process industry, energy, pharmaceuticals or civil engineering. Engineers working in AI, predictive maintenance or monitoring will find it a valuable deepening of their analytical skills.

Throughout the training, you will develop a fully functional analysis tool in Python with a graphical interface.
This tool allows you to visualise, filter and interpret signals in your own work environment. By the end of the course, you will take it with you — ready to apply directly in your daily engineering projects.