Research and Diagnostics Laboratory of Pressure Resistive Sensors for Composites

Zelnik R., Bozek P., Kamenszka A.

Abstract


The use of sensors prevents the shortened service life, wear and tear, and decreased accuracy due to degradation during operation. For sensors prone to inaccuracy, a “sensor-device-program” diagnostic assembly has been created. Such a circuit is capable of autonomous diagnosis, calibration and evaluation, up to and including autonomous recalibration of sensors. The diagnostic device also has a shock test function. The purpose of the operation is to deliberately increase the life and accuracy of the sensor under test. The diagnostic device is designed for testing under laboratory conditions and verifies the correctness of the diagnostic algorithm. The result of diagnostics is a report on the current state of the sensor and the changes compared to the past states. The current state includes estimates of accuracy, range, sensitivity or error parameters such as strain constant, maximum Po value and others. Thus, the degradation of selected parameters can be monitored and a mathematical calculation of the results can be applied to possibly improve/correct the sensor errors. By recording force levels, it will be known what force was applied to the sensor during the measurement and thus protects against damage from overloading. The maximized life is achieved through a combination of the accuracy control, calibration performance and error estimation. As a result, the conventional industrial sensor will be a reliable tool for industrial measurements, not just laboratory measurements.

Keywords


sensor; diagnostics; calibration; analysis; control algorithm

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References References

Daus H. Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder. JMIR Mental Health, 2020, vol. 7, no. 7.

Domnina K., Pivarciova E. Mnogofaktornyy podkhod k polucheniyu yacheistykh kompozitsionnykh materialov [Multifactorial approach for obtaining cellular composite materials]. Ostrava, Amos Publ., 2021 (in Russ.). ISBN: 978-80-87691-39-7.

Abramov I. Diagnostics of electrical drives. In The 18th International Conference on Electrical Drives and Power Electronics. EDPE 2015, The High Tatras, Slovakia, 2015, pp. 364-367.

Bajracharya S., Sasaki E. Investigation of effect of stress on eddy current response using phase diagram. Structure and Infrastructure Engineering, 2020, vol. 16, no. 9, pp. 1276-1285.

Christiansen J.M., Smith G.E. Development and Calibration of a Low-Cost Radar Testbed Based on the Universal Software Radio Peripheral. IEEE Aerospace and Electronic Systems Magazine, 2019, vol. 34, no. 12., pp. 50-60.

Frankovsky P. Experimental analysis of stress fields of rotating structural elements by means of reflection photoelasticity. Applied optics, 2017, vol. 56, Issue 11, pp 3064-3070.

Kadiyala E. Global industrial process monitoring through IoT using Raspberry pi. International conference on nextgen electronic technologies, 2017, pp. 260-262.

Bozek P. Diagnostics of Mechatronic Systems. Springer, Series: Studies in Systems, Decision and Control, 2021, vol. 345.

Abramov I. Monitoring of technical condition of motors and bearings of woodworking equipment. Acta Facultatis Xylologiae Zvolen, 2014, vol. 56, Issue 2, pp. 97-104.

Bishop P., Povyakalo A. A conservative confidence bound for the probability of failure on demand of a software-based system based on failure-free tests of its components. Reliability Engineering and System Safety, 2020, vol. 203.

Bucinskas V. Research of the New Type of Compression Sensor. Automation, 2018, vol. 743, pp. 561-573.

Chudzikiewicz A., Sowinski B. Simulation method of selection of diagnostic parameters in the process of monitoring the rail vehicle's conditions. Structural health monitoring, 2011, vol. 1, pp. 1103-1110.

Dao A.T. Wireless laptop-based phonocardiograph and diagnosis. Peer J, 2015, vol. 3. doi: 10.7717/peerj.1178

Farhat A. Impacts of wireless sensor networks strategies and topologies on prognostics and health management. Journal of Intelligent Manufacturing, 2019, vol. 30, no. 5, pp. 2129-2155.

Li J. A Remote Monitoring and Diagnosis Method Based on Four-Layer IoT Frame Perception. IEEE Access, 2019, vol. 7, pp. 144324-144338.

Lopez Alcala J.M. User-Printable Three-Rate Rain Gauge Calibration System. Frontiers in Earth Science, 2019, vol. 7.

Maslakova K. Applications of the strain gauge for determination of residual stresses using Ring-core method. Procedia Engineering, 2012, vol. 48, pp. 396-400.

Muller R. Data or interpretations: Impacts of information presentation strategies on diagnostic processes. Human Factors and Ergonomics In Manufacturing, 2020, vol. 4, no. 4, pp. 266-281.

Yan L. Shock tube-based calibration installation for dynamic pressure transducers and performance testing. Journal of Engineering - JOE, 2019, vol. 2019, Issue 23, pp. 8577-8582.

Nikitin Y. Logical - Linguistic Model of Diagnostics of Electric Drives with Sensors Support. Sensors, 2020, vol. 20, no. 16, pp. 1-19.

Tlach V. Determination of the Industrial Robot Positioning Performance. 13th International Conference on Modern Technologies in Manufacturing (MTeM-AMaTUC), vol. 137. Cluj Napoca, MATEC, 2017.

Krivoulya G. Expert diagnosis of computer systems using neuro-fuzzy knowledge base. Proceedings of 2016 IEEE East-West design & test symposium, 2016.




DOI: http://dx.doi.org/10.22213/2413-1172-2021-4-11-16

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ISSN 1813-7903 (Print)
ISSN 2413-1172 (Online)