An effective data reduction model for machine emergency state detection from big data tree topology structures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F21%3A63544478" target="_blank" >RIV/70883521:28140/21:63544478 - isvavai.cz</a>

Result on the web

<a href="https://sciendo.com/article/10.34768/amcs-2021-0041" target="_blank" >https://sciendo.com/article/10.34768/amcs-2021-0041</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.34768/amcs-2021-0041" target="_blank" >10.34768/amcs-2021-0041</a>

Result language

angličtina

Original language name

An effective data reduction model for machine emergency state detection from big data tree topology structures

Original language description

This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an elastic hierarchical system for effective data reduction and classification, which encompasses several modules. Firstly, principal component analysis (PCA) is used to perform data reduction of many input signals from big data tree topology structures into two signals representing all of them. Then the technique for segmentation of operating machine data based on dynamic time distortion and hierarchical clustering is used to calculate signal accident characteristics using classifiers such as the maximum level change, a signal trend, the variance of residuals, and others. Data segmentation and analysis techniques enable effective and robust detection of operating machine tool anomalies and emergency states due to almost real-time data collection from strategically placed sensors and results collected from previous production cycles. The emergency state detection model described in this paper could be beneficial for improving the production process, increasing production efficiency by detecting and minimizing machine tool error conditions, as well as improving product quality and overall equipment productivity. The proposed model was tested on H-630 and H-50 machine tools in a real production environment of the Tajmac-ZPS company.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Applied Mathematics and Computer Science

ISSN

1641-876X

e-ISSN

—

Volume of the periodical

31

Issue of the periodical within the volume

4

Country of publishing house

PL - POLAND

Number of pages

11

Pages from-to

601-611

UT code for WoS article

000740632100005

EID of the result in the Scopus database

2-s2.0-85123790266