Validation method of error estimation of the metering device based on Monte Carlo simulation

Qingchan Liu; Yao Zhong; Cong Lin; Tengbin Li; Guangrun Yang; Junchao Chang

doi:10.1117/12.3015193

4 March 2024 Validation method of error estimation of the metering device based on Monte Carlo simulation

Qingchan Liu, Yao Zhong, Cong Lin, Tengbin Li, Guangrun Yang, Junchao Chang

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129815D (2024) https://doi.org/10.1117/12.3015193
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

At present, amid the error estimation analysis of metering devices in plants and stations, the model cannot be verified reasonably because of the sparsity of valid samples, and then the accuracy and applicability of the constructed model cannot be evaluated. To solve this problem, this paper proposes a method to verify the validity of error estimation of the metering device based on Monte Carlo simulation. By using the T-test, F-test, KS-test, and other statistical test methods to test and analyze the data distribution in the source data, Monte Carlo simulation technology is used to generate simulation data distributed with the real source data. On this basis, the out-of-tolerance samples under the simulation data and the real data are constructed to realize the effective verification of the model. The results show that the proposed validity verification method can reasonably compensate for and solve the problem that the model cannot be validated effectively owing to the sparsity of valid samples.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingchan Liu, Yao Zhong, Cong Lin, Tengbin Li, Guangrun Yang, and Junchao Chang "Validation method of error estimation of the metering device based on Monte Carlo simulation", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129815D (4 March 2024); https://doi.org/10.1117/12.3015193

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Monte Carlo methods

Statistical analysis

Error analysis

Computer simulations

Instrument modeling

Sampling rates

Show All Keywords

Keywords/Phrases

Search In:

Publication Years