| Subject name (in Hungarian, in English) | Measurement Theory and Technique I. (complex exam) | |||
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Metrology I.
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| Neptun code | BMEGEMIDMT1 | |||
| Type | study unit with contact hours | |||
| Course types and number of hours (weekly / semester) | course type: | lecture (theory) | exercise | laboratory excercise |
| number of hours (weekly): | 1 | 1 | 0 | |
| nature (connected / stand-alone): | - | coupled | - | |
| Type of assessments (quality evaluation) | exam | |||
| ECTS | 3 | |||
| Subject coordinator | name: | Dr. Samu Krisztián | ||
| post: | associate professor | |||
| contact: | samuk@mogi.bme.hu | |||
| Host organization | Department of Mechatronics, Optics and Mechanical Engineering Informatics | |||
| http://www.mogi.bme.hu/ | ||||
| Course homepage | http://www.mogi.bme.hu/tantargyak/BMEGEMIDMT1 | |||
| Course language | hungarian | |||
| Primary curriculum type | komplex vizsga tárgycsoport PhD tárgy | |||
| Direct prerequisites | Strong prerequisite | none | ||
| Weak prerequisite | ||||
| Parallel prerequisite | ||||
| Milestone prerequisite | at least obtained 0 ECTS | |||
| Excluding condition | none | |||
Aim
The aim of the subject is to measure the physical quantities typically found in technical systems and to process the measurement data. Systematization of errors, their nature, their origin, and ways to reduce their impact. Electrical measurement of time-varying non-electrical quantities. Structure of the measuring chain that can be built for research purposes, selection of sensors and signal converters, measuring procedures. Dynamic and frequency transmission errors. Fundamentals of signal frequency analysis. Digital measurement technology and sampling. The role of technical information obtained by measurement in technical scientific research (I). Measurement as a modeling process. Traditional and modern models of measurement. Information theory model of measurement. Information theory issues in measurement technology: the amount of information that can be obtained by measurement, the entropy of error. Systematization of the origin of errors, theoretical and technical possibilities of error reduction.
Learning outcomes
Competences that can be acquired by completing the course
Knowledge
Student knows the role of measurement and instrumentation in research and mechatronics. Student identifies the elements of the measuring chain and the relationship between the operation of the sensors - the physical principle and the measurable quantity. Student knows the correct form of the measurement result, the methods of processing the measurement data: grouping of measurement data, rounding rules, choosing the level of reliability. Student identifies methods for calculating standard deviation and error propagation. Student is familiar with measurement organizations and the SI system. Student understands the concepts of calibration and official calibration, as well as the rules of traceability and derivability, as well as the principles of analysis and classification of measurement errors: by origin, nature and form. Student is aware of the basic features of the Hungarian energy statistical data collection system. Student understands the problems of measuring time-varying physical quantities in a time and frequency domain. Student distinguishes between measurement errors (also through examples) and their cause, properties and how to eliminate them. Student understands signal organization and the spectrum of basic signal types and sampling.
Ability
Student able to identify areas of measurement technology related to technical research. Student selects the appropriate statistics methods for a given task. Student designs the data collection strategy required for a given task. Student describes the elements of the mechatronic and metrological measuring chain. Student applies the rules for giving the measurement result and the methods for calculating the error propagation and uncertainty. Student able to identify measurement tasks for constant and variable quantities over time and assign measurement sensors to them. Student describes the basic signal types and the tasks of calibrating the first and second order measuring system. Student selects the statistical method that best fits the research task. Using statistical data processing software, student selects the method of processing the measurement results. Student able to prepare research measurement reports and documentation in an accurate and professionally impeccable form.
Attitude
Student seeks to collaborate with the lecturer and fellow researchers in expanding knowledge. Student expands their measurement theory knowledge by continuously acquiring knowledge. Student is open to the use of software statistical data processing systems. Student is open for the use of automated measuring systems. Student is open to learning about new sensors and measurement systems in measurement technology.
Independence and responsibility
Student accepts guidelines for measurements and protocols for research purposes. Student feels responsible for the work of their colleagues during the measurement activity in the measurement team. Student is responsible for the careful execution of the measurement activity. Student evaluates the measurement tasks with full care and on time. When solving professional problems, student checks the compliance of the measurements with legal regulations.
Teaching methodology
Lectures, measuring instrument demonstrations, laboratory measurements, electronic data processing, selection and processing of literature, preparation of measurement protocols and project assignments related to the research. Preparation of a study or project task in connection with the use of measurement technology for scientific purposes - by performing, documenting and evaluating basic measurements. Exploration and calculation of measurement errors and uncertainties.
Support materials
Textbook
Hütte: Handbook of Engineering Sciences - Chapter H. Springer, ISBN: 9637775501, 2012.
Hunyadi L. - Vita L .: Statistics Statistics County: I.-II. Alua Publisher 2008. ISBN 9789639698246
Lecture notes
Dr. Gábor Halász, Dr. Antal Huba: Technical measurements. University of Technology Publishing House, 2005
Online material
http://www.tankonyvtar.hu/hu/tartalom/tamop425/0029_2A_Merestechnika - Czifra, Drégelyi-Kiss, Galla, Huba, Kis, Petróczky: Technical measurements, 2012.
http://www.mogi.bme.hu/tantargyak/BMEGEMIDMT1
Validity of the course description
| Start of validity: | 2020. February 10. |
| End of validity: | 2024. December 31. |
General rules
During the academic period, learning outcomes are assessed by a mid-term summative assessment in written form (project report), which is a condition of 'signed' mark. This summative assessment is a complex, written way of evaluating the competence-type elements of the subject and knowledge in the form of reporting documentation, which requires the necessary theoretical knowledge and application skills and abilities. The course ends with an exam. The condition for obtaining the credit is that the student completes the mid-year summative assessment at the 40% level, together with any repetitions, corrections and replacements.
Assessment methods
Detailed description of mid-term assessments
| Mid-term assessment No. 1 | ||
| Type: | summative assessment | |
| Number: | 1 | |
| Purpose, description: | The basic goal of summative assessment is to examine the existence of knowledge, ability, attitude, and learning outcomes belonging to the autonomy and responsibility competence group. The way to do this is a literature analysis task. The aim of the task is for students to search for and present the data processing methods found in the literature in the case of a database from their research. By the time of the exam, the project task must be supplemented with statistical analysis and evaluation! Student will get 100 points if successfully complete the task and a minimum of 40 points must be achieved for successful completion. | |
Detailed description of assessments performed during the examination period
Elements of the exam:
| Written partial exam | ||
| Obligation: | mandatory (partial) exam unit, failing the unit results in fail (1) exam result | |
| Description: | Submission of the presentation and documentation of the project task prepared within the framework of the mid-year summative assessment in electronic and printed form. The documentation or study shall include a complete description of the measurement process of a measurement theory task or scientific research. With accompanying drawings, calculations, and data evaluations. Includes error calculation and enhancements; | |
| Oral partial exam | ||
| Obligation: | optional (partial) exam unit, which can be taken into account only if it is favourable for the student | |
| Description: | Submission of the presentation and documentation of the project task prepared within the framework of the mid-year summative assessment - then oral presentation of it. The presentation documentation or study contains a complete description of the measurement process of a measurement theory task or scientific research. With accompanying drawings, calculations, and data evaluations. Includes error calculation and enhancements; | |
The weight of mid-term assessments in signing or in final grading
| ID | Proportion |
|---|---|
| Mid-term assessment No. 1 | 100 % |
The condition for signing is that the score obtained in the mid-year assessments is at least 40%.
The weight of partial exams in grade
| Type: | Proportion |
|---|---|
| Written partial exam | 95 % |
| Oral partial exam | 5 % |
Determination of the grade
| Grade | ECTS | The grade expressed in percents |
|---|---|---|
| very good (5) | Excellent [A] | above 90 % |
| very good (5) | Very Good [B] | 85 % - 90 % |
| good (4) | Good [C] | 70 % - 85 % |
| satisfactory (3) | Satisfactory [D] | 55 % - 70 % |
| sufficient (2) | Pass [E] | 40 % - 55 % |
| insufficient (1) | Fail [F] | below 40 % |
The lower limit specified for each grade already belongs to that grade.
Attendance and participation requirements
The lack of the value means that there is no attendance requirement.
At least 70% the exercises (rounded down) must be actively attended.
Special rules for improving, retaken and replacement
The special rules for improving, retaken and replacement shall be interpreted and applied in conjunction with the general rules of the CoS (TVSZ).
| Need mid-term assessment to invidually complete? | ||
| yes | ||
| The way of retaking or improving a summary assessment for the first time: | ||
| each summative assessment can be retaken or improved | ||
| Is the retaking-improving of a summary assessment allowed, and if so, than which form: | ||
| retake or grade-improving exam not possible | ||
| Taking into account the previous result in case of improvement, retaken-improvement: | ||
| new result overrides previous result | ||
Study work required to complete the course
| Activity | hours / semester |
|---|---|
| participation in contact classes | 28 |
| mid-term preparation for practices | 7 |
| preparation for summary assessments | 16 |
| exam preparation | 21 |
| additional time required to complete the subject | 22 |
| altogether | 94 |
Validity of subject requirements
| Start of validity: | 2020. February 10. |
| End of validity: | 2024. December 31. |
Primary course
The primary (main) course of the subject in which it is advertised and to which the competencies are related:
Mechanical engineering sciences PhD programme
Link to the purpose and (special) compensations of the Regulation KKK
This course aims to improve the following competencies defined in the Regulation KKK:
Knowledge
Ability
Attitude
Independence and responsibility
Prerequisites for completing the course
|
Knowledge type competencies
(a set of prior knowledge, the existence of which is not obligatory, but greatly facilitates the successful completion of the subject) |
statistical knowledge in research, instrumentation and mathematical knowledge |
|
Ability type competencies
(a set of prior abilities and skills, the existence of which is not obligatory, but greatly contributes to the successful completion of the subject) |
preparation of research measurement documentation |