ZDA Data processing

Institute of Technology and Business in České Budějovice
winter 2024
Extent and Intensity
1/2. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Robert Frischer, Ph.D. (lecturer)
doc. Ing. Ivo Špička, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Robert Frischer, Ph.D.
Department of Applied Technologies and Materials Research – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Supplier department: Department of Applied Technologies and Materials Research – Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Timetable of Seminar Groups
ZDA/P01: Tue 8:45–9:30 I314, R. Frischer, I. Špička
ZDA/S15a: Tue 9:40–11:10 I314, R. Frischer, I. Špička
ZDA/S15b: Tue 13:05–14:35 I314, R. Frischer, I. Špička
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 40 student(s).
Current registration and enrolment status: enrolled: 35/40, only registered: 0/40
fields of study / plans the course is directly associated with
Course objectives supported by learning outcomes
The course focuses on the acquisition of key skills and methodologies needed to effectively acquire, analyse and presentation of data in a variety of data formats. Students will learn practical data processing using modern software tools especially in MATLAB environment, which includes data cleaning, statistical analysis, visualization data, and the application of these skills in real-world projects in industry. The course emphasizes the importance of ethical considerations when working with data and prepares students to use analytical tools for decision support in a corporate environment. Educational objectives are achieved through a combination of theoretical lectures and practical exercises, which enable students to apply theory to specific data sets and problems.
Learning outcomes
The graduate is able to apply system maintenance in a number of production processes based on the processing of operational data and is able to define its impact on machine uptime and production efficiency.
Syllabus
  • 1. Introduction to data processing
  • 2. Importing data into MATLAB and its structure
  • 3. Data cleaning and preprocessing
  • 4. Numerical filters and their effect on data series
  • 5. Basics of statistical analysis
  • 6. Data visualization in MATLAB
  • 7. Fundamentals of machine learning for data processing
  • 8. Advanced data analysis methods
  • 9. Searching in data series
  • 10. Advanced numerical filters for matrix variables
  • 11. Use of operational data for technical maintenance in industrial plants
  • 12. Predictive maintenance and its importance in industry
  • 13. Impact of maintenance on OEE
Literature
    required literature
  • Chi-Wah K., Wing-Shan T. 2024. Digital Image Denoising in MATLAB. Wiley-IEEE Press. ISBN 1119617693.
  • DINGYÜ, X., FENG, P. 2024. MATLAB and Simulink in Action: Programming, Scientific Computing and Simulation. Springer. ISBN 978-9819911752.
  • RINKAL, P. Matlab for Beginners. 2024. LAP LAMBERT Academic Publishing. ISBN 6207468856.
    recommended literature
  • BARLOW, M., 2015. Predictive Maintenance. O'Reilly Media, Inc. ISBN 9781491921104
  • SVOBODA, M., M. GANGUR a K. MIČUDOVÁ, 2019. Statistické zpracování dat. Západočeská univerzita v Plzni. ISBN 978-80-2610-883-2
  • NATHAN, M. and J. WARREN, 2015. Big Data: Principles and best practices of scalable realtime data systems. Manning Publications. ISBN 978-1617290343
  • SICILIANO, A., 2008. MATLAB: Data Analysis and Visualization. World Scientific. ISBN 9789812835543
  • HENDL, J, 2006. Praha: Portál. Přehled statistických metod zpracování dat. ISBN 80-7367-123-9
  • MOBLEY, K., M., 2013. An Introduction to Predictive Maintenance. Butterworth-Heinemann. ISBN 978- 0123996374
Forms of Teaching
Lecture
Seminar
Assessment Methods and Assesment Rate
Test – mid-term 30 %
Test – final 70 %
Seminary Work 30 %
Exam conditions
For successful completion of the course it is necessary to achieve a minimum of 70% in the interim and final assessment under the conditions set out below. 30 points can be obtained in the intermediate assessment, i.e. 30%. A total of 70 points, i.e. 70 %, may be obtained in the final assessment.

Interim evaluation Term paper/Interim test - 30 points (i.e. 30%)

Final Evaluation Final Test - 70 points (i.e. 70%) A full-time student is required to meet the mandatory 70% attendance in contact classes, i.e. everything except lectures.

Language of instruction
Czech
Teacher's information
Attendance at classes in all forms is dealt with in a separate internal standard of the VŠTE (Recording of student attendance at the VŠTE). For full-time students, 70% attendance is mandatory at contact classes, i.e. everything except lectures. Total course classification, i.e. points for the test (70 - 0) + points from the continuous assessment (30 - 0): A 100 - 90, B 89,99 - 84, C 83,99 - 77, D 76,99 - 73, E 72,99 - 70, FX 69,99 - 30, F 29,99 - 0.
  • Enrolment Statistics (recent)
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