The fundamental part gives knowledge of the theoretical background of signal analysis, e.g., Fourier analysis, FFT, DFT, correlation, signals and linear systems and the Z-transforms and digital filters. In the application part signal analysis is used in some important applications.
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Content and learning outcomes
Fundamental part: Amplitude characterisation, classification of signals, Fourier analysis and Laplace transforms in signal analysis, discrete signals (sampling, averaging, DFT, FFT, correlation methods, signals and linear systems - frequency response functions, power spectral density, frequency analysis with FFT, frequency analysis with filters, Z-transforms and digital filtering.
The students will get practical training in using the theoretical concepts and signal analysis methods by computer exercises.
Application part: Control of mechanical systems, machine monitoring, active control of sound and vibration. In the computer exercise in machine monitoring it is shown how vibration measurements can be used to detect defects in bearings. In the laboratory exercise on active control of sound digital filtering is used to attenuate sound in a duct.
Intended learning outcomes
The aim of the first part of the course is to give the students knowledge about the theoretical foundations of signal analysis, and ability to apply this knowledge for analysis of mechanical systems. The aim of the application part of the course is to acquire knowledge and practical ability in important methods in analysis of mechanical systems.
The student should after finishing the course be able to:
- Use a signal analyser (FFT-analyser) and be able to choose the measurement setup: frequency range, length of time record, time windows, number of averages etc.
- Perform signal analysis on measured time record in Matlab.
- Choose appropriate signal analysis methodology for a given problem. For example choosing between time or frequency domain analysis, one-channel or multi-channel analysis, different types of filtering etc.
- Interpret results from different types of signal analysis, for instance spectra, correlation functions or frequency response functions.
- Be able to extract information about the character of the studied signal such as periodicity, time delays and linearity.
Literature and preparations
Completed courses that have provided knowledge in mathematics equivalent to the content of course SF1682 Analytical and numerical methods for differential equations. Requirements in English 6 / b.
SD1120 Sound and Vibration 9.0 credits
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- LAB1 - Laboratory Works, 2.0 credits, grading scale: P, F
- LAB2 - Laboratory Works, 1.0 credits, grading scale: P, F
- TEN1 - Control Test, 3.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Other requirements for final grade
Written examination (TEN1; 3 university credits). Computer exercises (LAB1; 2 university credits), Laboratory exercise (LAB2; 1 university credits).
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web SD2125
Main field of study
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SD2180 Non-Linear Acoustics
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