Version 1
: Received: 7 April 2020 / Approved: 8 April 2020 / Online: 8 April 2020 (05:21:58 CEST)
How to cite:
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Effects of Hard and Soft Equality Constraints on Reliability Analysis. Preprints2020, 2020040119. https://doi.org/10.20944/preprints202004.0119.v1
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Effects of Hard and Soft Equality Constraints on Reliability Analysis. Preprints 2020, 2020040119. https://doi.org/10.20944/preprints202004.0119.v1
Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; Da Silveira, Jr., L.G. Effects of Hard and Soft Equality Constraints on Reliability Analysis. Preprints2020, 2020040119. https://doi.org/10.20944/preprints202004.0119.v1
APA Style
Rofatto, V.F., Matsuoka, M.T., Klein, I., Roberto Veronez, M., & Da Silveira, Jr., L.G. (2020). Effects of Hard and Soft Equality Constraints on Reliability Analysis. Preprints. https://doi.org/10.20944/preprints202004.0119.v1
Chicago/Turabian Style
Rofatto, V.F., Mauricio Roberto Veronez and Luiz Gonzaga Da Silveira, Jr.. 2020 "Effects of Hard and Soft Equality Constraints on Reliability Analysis" Preprints. https://doi.org/10.20944/preprints202004.0119.v1
Abstract
The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.
Keywords
constraints; hypothesis testing; outlier detection; Monte Carlo; quality control; geodesy
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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