Extraction of calibration navigation blocks using monte carlo analysis

Document Type : Research Paper

Authors

1 Ph.D Student, Amirkabir University of Technology

2 Professor, Amirkabir University of Technology

3 Assistant Professor, Shahrood University of Technology

Abstract

Todays, determining the maximum time it takes to keep navigation blocks in stock before re-calibration is one of the major concerns in the aerospace industry, as increasing this time helps to save a large part of costs. Reduce the transportation of blocks from the warehouse to the calibration laboratory and return them. On the other hand, reducing the storage time of the block in the warehouse, as it results in fewer changes in the calibration coefficients, will result in increased navigation accuracy when the blocks need to be used on the system. Therefore, choosing the right time for a compromise between cost and error rate will be essential. In this paper, by selecting a sensitivity analysis, called Monte Carlo analysis and having information on monthly calibration coefficients of navigation sensors over a period of one to two years, the calibration interval of navigation and navigation blocks is extracted analytically. Be. To achieve this goal, it is first shown that the sensitivity of the navigation error to the changes of the calibration coefficients is independent of the uncertainty range of the calibration coefficients, and then Monte Carlo analysis for each of the bias and scale factors of the various sensor blocks and a sampled navigation block. Finally, the results of this analysis show the relationship between the storage time of the block in the warehouse and the maximum error uncertainty.

Keywords

References

[1] O. Costenobble, G. Pontoriero, International Standadrd, Measurement Systems-Requirements for Measurement Processes and Measuring Equipment, Iso 10012, 2003.
[2] N. Natanilova, N. Ilina, E. Frantcuzskaia, Calibration Interval Adjustment of a Measuring Instrument in Industries During Long-Term Use, IV International Conference on Modern Technologies for Non- Destructive Testing, 2016.
[3] G. Pontoriero, Devices for determining the interlayer determination of  measurement equipment used in laboratories, OIML Organization International Metrologi, 1984.
[4] G. Panfilo, Establishment and Adjustment of Calibration Intervals, Recommended Practice, 1996. 
[5] E. Nunzi, G. Panfilo, P. Tavella, P. Carbone, D. Petri,  Stochastic and Reactive Methods for the Determination of Optimal Calibration Intervals, IEEE Transactions On Instrumentation And Measurement, Vol. 54, No. 4, 2005
[6] J. W. Chaffee, Relating the Allan and inertial variance to the diffusion coefficients of a linear stochastic differential equation model for precision oscillators, IEEE Trans. Ultrasonics Ferroelectrics and Frequency, Vol. 34, No.6, pp. 655-658,  1987.
[7] D. R. Cox, M. D. Miller, The Theory of Stochastic Processes. London, U.K.: Chapman &Hall, 1965.
[8] P. Carbone, Performance of simple response method for the establishment and adjustment of calibration intervals, IEEE Transactions on  Instrumentation and. Measurement, Vol. 53, No. 3, pp.730-735, 2004.  
Aerospace Knowledge and Technology Journal
Volume 9, Issue 2 - Serial Number 2
October 2020
Pages 153-163

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