Module also offered within study programmes:
General information:
Name:
Identification and signals analysis
Course of study:
2013/2014
Code:
RMS-1-602-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
First-cycle studies
Specialty:
-
Field of study:
Mechatronics with English as instruction languagege
Semester:
6
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
prof. dr hab. inż. Staszewski Wiesław (w.j.staszewski@agh.edu.pl)
Academic teachers:
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Understands the need of continuous knowledge updating Activity during classes,
Involvement in teamwork
Skills
M_U001 Can perform sampling of time-continuous signals Report,
Execution of laboratory classes,
Test results
M_U002 Can perform basic spectral signal analysis using suitable instruments and Matlab Report,
Execution of laboratory classes,
Test results
M_U003 Can perform identification of simple second-order systems Report,
Execution of laboratory classes,
Test results,
Completion of laboratory classes
Knowledge
M_W001 Has basic knowledge of signal and system description in time domain Execution of laboratory classes,
Completion of laboratory classes,
Test results
M_W002 Has basic knowledge of signal and system description in frequency domain Execution of laboratory classes,
Test results,
Completion of laboratory classes
M_W003 Knows and understands sampling effects of continuous-time signals Execution of laboratory classes,
Test results,
Completion of laboratory classes
M_W004 Has basic knowledge of analog filters and mechanical system models Execution of laboratory classes,
Test results,
Completion of laboratory classes
M_W005 Has basic knowledge of nonparametric spectrum estimation methods Execution of laboratory classes,
Test results,
Completion of laboratory classes
M_W006 Has basic knowledge of system identification using frequency methods and modal analysis Execution of laboratory classes,
Test results,
Completion of laboratory classes
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Others
Zaj. terenowe
Zaj. warsztatowe
E-learning
Social competence
M_K001 Understands the need of continuous knowledge updating + - + - - - - - - - -
Skills
M_U001 Can perform sampling of time-continuous signals + - + - - - - - - - -
M_U002 Can perform basic spectral signal analysis using suitable instruments and Matlab + - + - - - - - - - -
M_U003 Can perform identification of simple second-order systems + - + - - - - - - - -
Knowledge
M_W001 Has basic knowledge of signal and system description in time domain + - + + - - - - - - -
M_W002 Has basic knowledge of signal and system description in frequency domain + - + - - - - - - - -
M_W003 Knows and understands sampling effects of continuous-time signals + - + - - - - - - - -
M_W004 Has basic knowledge of analog filters and mechanical system models + - + - - - - - - - -
M_W005 Has basic knowledge of nonparametric spectrum estimation methods + - + - - - - - - - -
M_W006 Has basic knowledge of system identification using frequency methods and modal analysis + - + - - - - - - - -
Module content
Lectures:

1. Introduction to signals and systems
• Deterministic & stochastic signals
• Energy and power signals
• Continuous- and discrete-time signals
• Sampling and coding
• Linear time invariant (LTI) systems
2. Time domain analysis
• Dirac delta, time domain models
• Identification in time domain, impulse response
• Convolution model
• Stability and causality
3. Frequency domain analysis
• Fourier transform
• Frequency response, Bode diagram
• Modeling mechanical systems
• Time-frequency analysis
4. Sampling and Laplace transform
• Sampling of time-continuous signals
• Laplace transform, transfer function
• Poles and zeros, stability
• Analog filters
5. Discrete Fourier transform
• Truncation in time
• Rectangular window and sinc function
• Discrete Fourier transform (DFT)
• Nonparametric spectral estimation (periodogram)
6. Introduction to modal analysis
• Modal models
• Frequency response function
• Excitation techniques
• Frequency domain decomposition

Laboratory classes:

Introduction to Matlab
Signal processing in time domain
Modeling of mechanical systems
Signal processing in frequency domain
Time-frequency methods and wavelets
Elements of linear algebra
Regression models
Method of least squares
Prediction error method
Nonparametric identification
Evaluation of estimators’ performance
Model analysis – presentation

Project classes:
-
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 105 h
Module ECTS credits 4 ECTS
Participation in lectures 15 h
Participation in laboratory classes 30 h
Preparation for classes 30 h
Preparation of a report, presentation, written work, etc. 30 h
Additional information
Method of calculating the final grade:

Based on laboratory results (marks)

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:
  • J. S. Bendat, A.G. Piersol, Random Data: Analysis & Measurement Procedures, John Willey and Sons, New York, 2000
  • B. Mulgrew,P. Grandt, J. Thompson, Digital Signal Processing, Concepts and applications, Palgrave Macmillan, Second edition, 2003
  • R.B. Randall, Frequency Analysis, Brüel&Kjær, 1987
  • S. Braun, Discover signal processing. An interactive guide for engineers, Wiley, 2008.
Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None