With the ongoing digitalization of the industry towards "Industry 4.0" i.e. smart factories with more efficient production, shorter lead times, higher quality, etc. increase the need to measure different quantities and associated processing of this measurement data. This includes the collection of signals via different types of sensors and signal processing of these signals in order to e.g. provide "smart industrial cyber-physical systems" (SICPS) with information for the purpose of monitoring, maintaining, controlling activities, etc. in order to achieve smart factories.
But this requires both that the sensors that are selected enable the transfer of the information required for the application and that the analog-to-digital conversion enable the transfer of the information required for the application to the digital domain. In addition to this, in many cases "feature" extraction or signal processing of the digital version of a sensor signal can be carried out to produce a digital signal (sensor data) that is much easier to interpret, i.e. you "only" enhance information in the signal / data that is of interest for SICPS.
The course will focus on sensors, analog-to-digital converters, quality of measured data and signal processing where we, among other things, highlights the choice of sensors and data collection systems and introduces some robust signal processing methods in the light of the digitization of the industry.
This course is for professionals who work professionally with programming and development - regardless of industry or sector. You may be in the industry, the IT sector or a larger company in a completely different industry, you may be working in a development department within a company or as an IT consultant.
The course contains the following:
Sensors for measuring vibrations, force, elongation, speed and associated signal conditioning
Analog-to-digital converter and "Effective Number Of Bits (ENOB)"
Folding when sampling signals and anti-folding filters
Fourier transform - Discrete Fourier transform
Stochastic processes and relevant statistical concepts
Power density spectrum, Power spectrum and associated systematic and random errors
There will be a kick-off and two workshops on site where we learn, among other things to perform measurements on a rotating machine and associated data collection as well as signal analysis with professional equipment. All other lectures are given online and recorded and will later be available online.
The course will be delivered in a flexible way to facilitate the combination of coursework with your ongoing professional commitments.
The total scope of the course is normally about 100 hours.
Language of instruction: English.
The course is free of charge and gives 4 higher education credits.
Registration can be done continuously until the start of the course.
Course location is Växjö.
The course may be offered again in the future; it depends on the expressions of interest we receive so if you are interested in the course but is not able to attend this fall please contact us so that we know.
27/4, 13-16: Introduction of the course, presentation of participants, fixation of times for lectures and laboratory experiments, introduction of the different course modules and introduction of the Fourier transform(LH)
4/5, 9-16: Fourier transform and sensors for measurement of vibration, force, strain, rotation speed and angle as well as corresponding signal conditioning. Also, measurement uncertainty will be addressed. (LH)
11/5, 13-16: Discrete Fourier transform, Aliasing when sampling signals and anti-aliasing filters (LH)
17/5, 13-16: Analogue-to-Digital converters and Effective Number Of Bits (ENOB) as well as Discrete Fourier transform (LH)
25/5, 13-16: Stochastic processes and relevant statistical concepts (LH)
31/5, 13-16: Power Spectral Density, Power Spectrum and corresponding systematic and random errors. Home assignment (LH)
8/6, 9-17: Laboratory experiments (RP, LH)
Week 24: Spare time
What competences do I need to apply?
Basic qualifications at advanced level in mechanical engineering or equivalent. Candidates with relevant work experience are also welcome to apply. Two years' relevant work experience is considered to correspond to one year of university studies at the bachelor's level, we can validate you competence if needed.