Automatic detection of outlier data received in multi-parametric capillary sensors of diesel fuels fit for use

M. Borecki; P. Prus; M. L. Korwin-Pawlowski; P. Doroz; J. Szmidt

doi:10.1117/12.2500289

1 October 2018 Automatic detection of outlier data received in multi-parametric capillary sensors of diesel fuels fit for use

M. Borecki, P. Prus, M. L. Korwin-Pawlowski, P. Doroz, J. Szmidt

Author Affiliations +

Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 108080A (2018) https://doi.org/10.1117/12.2500289
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland

Abstract

The multi-parametric capillary sensor with local sample heating has been shown as an effective tool for diesel fuel fit for use classification at the laboratory level of technology, where a trained operator performs the experiments. The sensor consists of disposable capillary optrode, head and measurement control unit. An increase of the technology level of the sensor requires automation of samples handling and implementing automatic rejection of uncertain outlier data. Such data uncertainty may come from variations of capillary optrode diameters, inaccuracy of optrode filling with sample, inaccuracy of corking the sample as well as inaccuracy of optrode positioning in the head. Mentioned inaccuracies of preparation of the measurement may lead to outlier data, which impacts the correctness of sample classification. In this paper automatic detection of outlier data received in multi-parametric capillary sensors of diesel fuels is proposed and examined with data collected by untrained and trained operators. Performed experiments show that direct statistical tools applied to raw data lead to improper results of outlier data pointing. The proper outlier data pointing taking place for raw data converted to vector pattern of data on the base of physical phenomena described by experimental data or with the use of analysis of first derivative of raw data characteristic points course.

Citation Download Citation

M. Borecki, P. Prus, M. L. Korwin-Pawlowski, P. Doroz, and J. Szmidt "Automatic detection of outlier data received in multi-parametric capillary sensors of diesel fuels fit for use", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108080A (1 October 2018); https://doi.org/10.1117/12.2500289

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