Signal analysis in mechanical engineering
The course is useful for people who need to utilise signals measured from industrial machines. The aim is to give a strong mathematical background to support the analysis of measurements as well as modelling, diagnosis, control and automation of machines.
The course consists of the most important mathematical methods of digital signal processing related to mechanical engineering, such as filters, numerical differentiation and integration, application of the discrete Fourier transform, calculation of signal features in time and frequency domains, envelope analysis and other cyclostationary methods, describing mechanical phenomena by means of random signals and basics of statistical pattern recognition. Exercises from example applications and on numerical signal processing are provided.
After the course, the student is able to evaluate the effects of signal processing and analysis on the usability of measurement data from machines. These skills are essential e.g. in machine diagnostics, control engineering, machine automation and robotics.
After the course, the student:
- Is familiar with the most important methods of signal analysis in the field of mechanical engineering.
- Knows in which areas of mechanical engineering these methods are fundamentally important.
- Understands the basic concepts relating to the sampling of time domain signals and the creation of their spectrums.
- Knows the most commonly used features in mechanical engineering measurements and understands their significance in describing mechanical quantities.
- Understands the basic concepts of discrete time signal processing and knows how software tools and programming languages are used to perform analyses.
- Understands what kind of mechanical phenomena can be identified by time and frequency domain analysis.
- Knows the most important effects of signal processing and analysis on the usability of measurement data in identifying the operating modes of a machine and in implementing machine control.
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