access icon free Genetic algorithm and least square method-based calibration of measurement apparatus

The calibration process is one of the methods for decreasing the measurement apparatus uncertainties. In this study, the sensor calibration is performed using the values reproduced by stan­dard setting devices. Generally, the calibration characteristics of measurement apparatus are taken in a polynomial form. In this study, a genetic algorithm (GA) is used for the measurement apparatus calibration purpose. As an example, GA-based calibration of a differential pressure gauge using standard pressure setting devices is examined. The calibration results, founded using the proposed GA, are compared with the results obtained via the classical least square method. As a result, the recommendations on how to get better calibration characteristics for the differential pressure gauge are given.

Inspec keywords: pressure measurement; calibration; genetic algorithms; least squares approximations; polynomials; measurement standards; measurement uncertainty; pressure gauges; pressure sensors

Other keywords: standard pressure setting devices; measurement apparatus calibration purpose; differential pressure gauge; sensor calibration; genetic algorithm; least square method-based calibration process; polynomial; GA-based calibration; measurement apparatus uncertainties

Subjects: Pressure measurement; Interpolation and function approximation (numerical analysis); Sensing and detecting devices; Pressure and vacuum measurement; Measurement standards and calibration; Measurement theory; Transducers; Measurement and error theory; Numerical approximation and analysis; Sensing devices and transducers; Measurement standards and calibration; Other topics in statistics; Optimisation techniques; Probability theory, stochastic processes, and statistics

References

    1. 1)
      • 6. Kim, M.-S., Yu, S.-B., Lee, K.-S.: ‘Development of a high-precision calibration method for inertial measurement unit’, Int. J. Prec. Eng. Manuf., 2014, 15, pp. 567575.
    2. 2)
      • 10. Rivera, J., Herrera, G., Chacón, M., et al: ‘Improved progressive polynomial algorithm for self-adjustment and optimal response in ıntelligent sensors’, Sensors, 2008, 8, pp. 74107427.
    3. 3)
      • 19. Jacobson, L., Kanber, B.: ‘Genetic algorithms in Java basics’ (Apress, New York, 2015).
    4. 4)
      • 20. Abdullayev, A.A., Gadzhiyev, Ch.M.: ‘Metrological support to data-acquisition systems for oil-product accounting’, Meas. Tech., 1993, 36, (9), pp. 977981.
    5. 5)
      • 1. Cooper, W.D.: ‘Electronic instrumentation and measurement techniques’ (Prentice-Hall, Inc., Englewood Cliffs, NJ, 1978).
    6. 6)
      • 16. Akcan, H.: ‘A genetic algorithm based solution to the minimum-cost bounded-error calibration tree problem’, Appl. Soft Comput. J., 2018, 73, pp. 8395.
    7. 7)
      • 4. Hajiyev, C.: ‘Sensor calibration design based on D-optimality criterion’, Metrol. Meas. Syste., 2016, 23, (3), pp. 413424.
    8. 8)
      • 13. Li, M., Ji, X., Liu, H., et al: ‘A sensor parameter calibration method of tire pressure monitor system based on linear regression technology’. 3rd Int. Conf. on Material, Mechanical and Manufacturing Engineering (IC3ME −2015), Guangzhou, China, 2015, pp. 10511056.
    9. 9)
      • 11. Qiang, W, Yong-feng, S., Yi-zhen, Y.: ‘Genetic algorithm for calibrating a three-axis measuring system’, J. Aerosp. Eng., 2012, 25, (3), pp. 431435.
    10. 10)
      • 14. Xueliang, P., Pei, J., Chunsheng, L.: ‘The calibration method of three-axis magnetometer based on genetic algorithm’, Appl. Mech. Mater., 2015, 722, pp. 373378.
    11. 11)
      • 15. Yoon, J.: ‘ANN-based collaborative sensor calibration and GA-approach to sensor mutation management’. 2017 6th IIAI Int. Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu, Shizuoka, Japan, 2017, pp. 897902.
    12. 12)
      • 3. Betta, G., Dell'Isola, M., Frattolillo, A.: ‘Experimental design techniques for optimising measurement chain calibration’, Measurement, 2001, 30, pp. 115127.
    13. 13)
      • 17. Michalewicz, Z.: ‘Genetic algorithms + data structures = evolution programs’ (Springer Verlag, Berlin, Heidelberg, NewYork, 1992, 3rd edn.).
    14. 14)
      • 5. Khan, S.A., Shabani, D.T., Agarwala, A.K.: ‘Sensor calibration and compensation using artificial neural network’, ISA Trans., 2003, 42, pp. 337352.
    15. 15)
      • 9. Brimacombe, J.M., Anglin, C., Hodgson, A.J., et al: ‘Validation of calibration techniques for tekscan pressure sensors’. ISB XXth Congress - ASB 29th Annual Meeting, Cleveland, Ohio, 2005, p. 263.
    16. 16)
      • 7. Tomczyk, K.: ‘Application of genetic algorithm to measurement system calibration intended for dynamic measurement’, Metrol. Meas. Syste., 2006, XIII, (1), pp. 93103.
    17. 17)
      • 8. Mozek, M., Vrtacnik, D., Resnik, D., et al: ‘Calibration and error correction algorithms for smart pressure sensors’. IEEE MELECON 2002, Cairo, Egypt, 2002, pp. 240243.
    18. 18)
      • 18. Cortes, P., Larraneta, J., Onieva, L.: ‘Genetic algorithm for controllers in elevator groups: analysis and simulation during lunch peak traffic’, Appl. Soft Comput., 2004, 4, (2), pp. 59174.
    19. 19)
      • 2. Vasilchenko, K.K., Leonov, V.A., Pashkovskiy, I.M., et al: ‘Flight testing of aircrafts’ (Mashinostroyeniye, Moscow, 1996, 2nd edn.), (in Russian).
    20. 20)
      • 12. Wang, Z., Li, Q., Wang, Z., et al: ‘Novel method for processing the dynamic calibration signal of pressure sensor’, Sensors, 2015, 15, pp. 1774817766.
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