Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Experimental Identification of Condition of Position Sensor

Tatiana Kelemenová*, Miroslav Dovica, Eduard Jakubkovič, Peter Sedlačko

Department of Biomedical Engineering and Measurement, Technical University of Kosice, Faculty of Mechanical Engineering, Kosice, Slovak Republic

Abstract: Paper deals with experimental identification of condition of potentiometer position sensor. Calibration characteristic is obtained. Uncertainty of measurement also has been evaluated.

Keywords: sensor, position, uncertainty, condition

Citation: Tatiana Kelemenová, Miroslav Dovica, Eduard Jakubkovič, Peter Sedlačko, Experimental Identification of Condition of Position Sensor. American Journal of Mechanical Engineering, 5 (6). 289-292. January 2017. doi:10.12691/ajme-5-6-11

1. Introduction

A Mechatronic product has integrated sensors, actuators, mechanical structures and control structures. Measurement of position is very frequently measured quantity in practice. The potentiometer sensor has a long tradition of using. Last years the using of this principle has increased, because of its disadvantage. A lot of books have mentioned about its disadvantages like noise, oxidation of wiper and resistive road, short life etc. In this days situation is changes, because the new technologies and materials have been developed. Modern potentiometer sensors have an excellent properties, low noise, long life, low uncertainty etc. This article deals with testing of one of these sensors.

Figure 1. Position sensor

2. Sensor Properties and Its Calibration

The sensor has mechanical travel 800 mm and total electrical resistance 10kΩ. Linearity error of the sensor should be less than 1%.

Calibration of the sensor has been executed in accordance with standards (EA-4-02rev01) [2]. Position of the wiper has been adjusted with length gauges. Length gauges has been composed into the block of the length gauges. Full range of the sensor has been compared with block of the gauges at every millimetre. Electrical resistance between wiper and one end has been assigned to every block of length gauges (every millimetre ten times).

It is recommended to do calibration for every millimetre ten times in industrial practise. Ten times measured every value is minimum, which enables to evaluate standard uncertainty of type A (see standards EA-4-02rev01) [2].

Two packages of the length gauges have to be used for the calibration process. The Gloves has been necessary for the manipulation with these gauges. Process needs very high attention and a lot of time. Temperature if the room has to be regulated via air condition at the 20°C. Sensor and package of the length gauges has to be placed in laboratory with stabilized air temperature all day before measurements. Every piece of the length gauges is conserved with vaseline to avoid the corrosion of the length gauges. So, every piece is necessary to unconserve with denatured alcohol before using.

Consequently, observance of every these mentioned rules causes that calibration process is very complicated and difficult for time.

Measured data have been stored into the evaluation table. It is possible to evaluate static characteristic shown on Figure 3.

Figure 2. Calibration procedure of position sensor

Table 1. Selected sensor characteristics

Measured data has nonlinear dependence. That is difference from information mentioned via producer noted in Table 1. Dependence can be fitted with polynomial of 2nd degree. Also maximum range of the output electrical resistance exceeds the mentioned total resistance 10kΩ.

This characteristic enables to recalculate the measured electrical resistance to linear position of the wiper from the end of the sensor. The approximation regression equation (shown on Figure 3) can be inserted into the evaluation subsystem for calculation of the measured position. But, how we can believe it? How is the measured data and equation exactly? It is necessary to give answers for these questions.

Figure 3. Position sensor static characteristic

3. Position Sensor Measurement Chain Uncertainty

The uncertainty of measurement is a parameter, associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand. Term uncertainty is also used for uncertainty of measurement if there is no risk of misunderstanding.

Sensor producer doesn’t note uncertainty of measurement. Consequently, it is necessary to obtain this information from calibration process.

For a random variable the variance of its distribution or the positive square root of the variance, called standard deviation, is used as a measure of the dispersion of values. The standard uncertainty of measurement associated with the output estimate or measurement result y, denoted by u(y), is the standard deviation of the measurand Y [2].

The uncertainty of measurement associated with the input estimates is evaluated according to either a 'Type A' or a 'Type B' method of evaluation. The Type A evaluation of standard uncertainty is the method of evaluating the uncertainty by the statistical analysis of a series of observations. In this case the standard uncertainty is the experimental standard deviation of the mean that follows from an averaging procedure or an appropriate regression analysis. The Type B evaluation of standard uncertainty is the method of evaluating the uncertainty by means other than the statistical analysis of a series of observations. In this case the evaluation of the standard uncertainty is based on some other scientific knowledge [2].

The Type A evaluation of standard uncertainty can be applied when several independent observations have been made for one of the input quantities under the same conditions of measurement (minimum of 10 samples of measurement). If there is sufficient resolution in the measurement process there will be an observable scatter or spread in the values obtained [2].

The proper use of the available information for a Type B evaluation of standard uncertainty of measurement calls for insight based on experience and general knowledge. It is a skill that can be learned with practice. Type B evaluation of standard uncertainty can be obtained from various sources as [2]:

• previous measurement data,

• experience with or general knowledge of the behaviour and properties of relevant materials and instruments,

• manufacturer’s specifications,

• data provided in calibration and other certificates,

• uncertainties assigned to reference data taken from handbooks.

Electrical resistivity has been measured via multimeter and manufacturer provides specification for type B evaluation of the standard uncertainty of measurement. It is possible to specify equation:

[picture] | (1)

Figure 4 shows the standard uncertainty of measurement for values of electrical resistance measured via multimeter. Type B evaluation is much smaller then types A evaluation. So, it is possible the evaluation B neglected in the next evaluation process. It means that multimeter used in calibration process has been well selected.

Figure 4. The standard uncertainty of measurement for electric resistance of the sensor measured in calibration process

Figure 5. The standard uncertainty of position measurement

Recalculation of the standard uncertainty of electrical resistance measurement to standard uncertainty of position measurement is possible via using regression math model obtained from analysis shown on Figure 3. Figure 5 shows the standard uncertainty for position measurement.

Within EAL it has been decided that calibration laboratories accredited by members of the EAL shall state an expanded uncertainty of measurement U, obtained by multiplying the standard uncertainty u(y) of the output estimate y by a coverage factor k [2],

[picture] | (2)

Coverage factor should be defined via sensor manufacturer, but datasheet has no information about it. Best way how to find value of coverage factor is experiment. It is known that coverage factor depends on measurement data distribution.

4. Identification of Measurement Data Distribution

Identification of the measurement data distribution has done for four random selected values from sensor range. Every value has been measured 100 times at the same conditions. These values have been evaluated into histograms shown on Figure 6.

All explored values are distributed according to Normal law of distribution of measured values. It means that for significance level P=0.95 is coverage factor equals to value 2.

Figure 6. Sensor measurement data distribution

Figure 7. The expanded uncertainty of position measurement

5. Conclusion

Figure 7 shows the expanded uncertainty for position measurement. Expanded uncertainty means the interval about mean value (obtained as average of measured data) where located true value of measurement with probability 95% is.

The expanded uncertainty means how we can believe to examine sensor in measurement process. The expanded uncertainty is as inseparable part of measurement result [3,4,5,6].

Acknowledgements

The work has been accomplished under the research projects No. VEGA 1/0182/15, KEGA 014STU-4/2015 and APVV-15-0149 financed by the Slovak Ministry of Education.

References

[1]   | A. M. Pawlak: Sensors and Actuators in Mechatronics. CRC Press. Taylor & Francis Group. 2007. . ISBN 0-8493-9013-3. 409s.
[2]   | S. Soloman: Sensors Handbook. Second Edition. The McGraw-Hill Companies, Inc. 2010. 1424p.
[3]   | EA-4/02 Expression of the Uncertainty of Measurement in Calibration. European co-operation Accreditation Publication Reference. December 1999.
[4]   | L. JURIŠICA, A. VITKO, F. DUCHOŇ, D. KAŠTAN, Statistical Approach to GPS Positioning of Mobile Robot. In: Control Engineering and Applied Informatics. 2010, Vol. 12, No. 2, p. 44-51, ISSN 1454-8658.

[5]  
A. VITKO, L. JURIŠICA, M. KĽÚČIK, R. MURÁR, F. DUCHOŇ, Sensor Integration and Context Detection in Mechatronic Systems. In: Mechatronika 2008: Proceedings of 11th International Conference on Mechatronics. Slovakia, Trenčianske Teplice, June 4-6, 2008. - Trenčín: Trenčianska univerzita Alexandra Dubčeka v Trenčíne, 2008. - ISBN 978-80-8075-305-4. - p. 49-53.

[6]   | D. Koniar, L. Hargaš and M. Hrianka, Application of standard DICOM in LabVIEW, Proc. of 7th conf. Trends in Biomedical Engineering, Kladno 11.-13. 9. 2007 ISBN 978-80-01-03777-5. 2007.
[7]   | A. Vitko, L. Jurišica, M. Kľúčik, R. Murár, F. Duchoň,: Embedding Intelligence Into a Mobile Robot. In: AT&P Journal Plus. ISSN 1336-5010. Č. 1 : Mobilné robotické systémy (2008), s. 42-44.
[8]   | P. Božek, Robot path optimization for spot welding applications in automotive industry, Tehnicki vjesnik / Technical Gazette. Sep/Oct2013, Vol. 20 Issue 5, p913-917. 5p.
[9]   | F. Duchoň, A. Babinec, M. Kajan, P. Beňo, M. Florek, T. Fico, L. Jurišica, Path planning with modified A star algorithm for a mobile robot, Procedia Engineering 96, 59-69.
[10]   | P. Pásztó, P. Hubinský, Mobile robot navigation based on circle recognition, Journal of Electrical Engineering 64 (2), 84-91.
[11]   | I. V. Abramov, Y. R. Nikitin, A. I. Abramov, E. V. Sosnovich, P. Božek, Control and Diagnostic Model of Brushless DC Motor, Journal of Electrical Engineering. Volume 65, Issue 5, P 277-282, 2014.
[12]   | D. Koniar, L. Hargaš, S. Štofan, Segmentation of Motion Regions for Biomechanical Systems, Procedia Engineering, Volume 48, 2012, Pages 304-311.
[13]   | Ľ. Miková, M. Kelemen, F. Trebuňa, I. Virgala, S. Medvecká-Beňová, experimental identification of piezo actuator characteristic. Metalurgija 54 (2015) 1, 221-223.
[14]   | Fatikow, S. & Rembold. U., Microsystem Technology and Microrobotics. Berlin Heidelberg, Springer-Verlag, (1997).
[15]   | V. Chudý, R. Palenčár, E. Kureková, M. Halaj,: Measurement of technical quantities (in Slovak). Vydavateľstvo STU, 1st. ed., 1999. ISBN 80-227-1275-2.
[16]   | JCGM 100 – Evaluation of measurement data – Guide to the expression of uncertainty in measurement (ISO/IEC Guide 98-3). First edition September 2008. Available online: http://www.iso.org/sites/JCGM/GUM-JCGM100.htm; http://www.bipm.org/en/publications/guides/gum_print.html.
[17]   | JCGM 104 – Evaluation of measurement data – An introduction to the "Guide to the expression of uncertainty in measurement" (ISO/IEC Guide 98-1). First edition July 2009. Available online: http://www.bipm.org/en/publications/guides/gum_print.html.
[18]   | JCGM 200 - International vocabulary of metrology – Basic and general concepts and associated terms (VIM) 3rd edition (2008 version with minor corrections). © JCGM 2012 Available online: http://www.iso.org/sites/JCGM/VIM-JCGM200.htm.
[19]   | F. Kreith, The Mechanical Engineering Handbook Series. CRC PRESS. New York. ISBN 0-8493-0866-6. 2508s.
[20]   | M. Meloun, J. Militký, 2004. Statistical analysis of experimental data. (In Czech) Praha: Academia, 2004, ISBN 80-200-1254-0.
[21]   | MSA 104/97 Expression of the Uncertainty of Measurement in Calibration. (EAL-R2) - Expression of the Uncertainty of Measurement in Calibration, Slovenská národná akreditačná služba, SNAS BRATISLAVA, december 1997.
[22]   | MSA 104/D1-98 Appendix 1 for MSA 104-97 Expressing of measurement uncertainties in Calibration (in Slovak) (EAL-R2-S1), (EA-4/02-S1) Supplement 1 to EAL-R2 Expression of the uncertainty of measurmement in calibration. Slovak national accreditation service, SNAS BRATISLAVA, október 1998.
[23]   | MSA-L/11 Guidelines on the expresion of uncertainty in quantitative testing (In Slovak) (EA - 4/16: 2003). Guidelines on the expresion of uncertainty in quantitative testing. Slovak national accreditation service, SNAS BRATISLAVA, august 2009.
[24]   | MSA–L/12 Expression of the uncertainty of measurement in calibration (In Slovak) (EA-4/02) - Expression of the uncertainty of measurement in calibration, Slovak national accreditation service, SNAS BRATISLAVA, november 2010.
[25]   | B. N. Taylor and C. E. Kuyatt, 1994, Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results, NIST Technical Note 1297.
[26]   | TPM 0050-92 Etalons. Expressing of errors and uncertainties. (In Slovak). Metrological Technical Directive. Slovak Metrological Institute. Bratislava, 1992.
[27]   | TPM 0051-93 Expressing of uncertainties in measurement. (In Slovak) Metrological Technical Directive. Slovak Metrological Institute. Bratislava, 1993.
[28]   | G. Wimmer, R. Palenčár, V. Witkovský, Stochastic models of measurement. (In Slovak) Graphic Studio Ing. Peter Juriga, Ľ. Fullu 13, 841 05 Bratislava. 1st. ed., 2001. ISBN 80-968449-2-X.
[29]   | R. Palencar, P. Sopkuliak, J. Palencar et al. Application of Monte Carlo Method for Evaluation of Uncertainties of ITS-90 by Standard Platinum Resistance Thermometer. Measurement Science Review. Volume: 17, Issue: 3 Pages: 108-116. Published: Jun 2017.

[30]  
P. Sopkuliak, R. Palencar, J. Palencar, et al. Evaluation of Uncertainties of ITS-90 by Monte Carlo Method. Conference: 6th Computer Science On-Line Conference (CSOC) Location: Zlin, CZECH REPUBLIC Date: APR, 2017. CSOC2017, VOL 2 Book Series: Advances in Intelligent Systems and Computing. Volume: 574. Pages: 46-56. Published: 2017.