<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Department of Informatics and Artificial Intelligence

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an...

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

Publié en ligne: 30 déc. 2021

Pages: 601 - 611

Reçu: 11 juin 2021

Accepté: 19 oct. 2021

DOI: https://doi.org/10.34768/amcs-2021-0041

Mots clés
OPC UA, OPC tree, PCA, big data analysis, data reduction, machine tool, anomaly detection, emergency states

© 2021 Iaroslav Iaremko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Publié en ligne: 30 déc. 2021

Pages: 601 - 611

Reçu: 11 juin 2021

Accepté: 19 oct. 2021

DOI: https://doi.org/10.34768/amcs-2021-0041

Mots clésOPC UA, OPC tree, PCA, big data analysis, data reduction, machine tool, anomaly detection, emergency states

© 2021 Iaroslav Iaremko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
OPC UA, OPC tree, PCA, big data analysis, data reduction, machine tool, anomaly detection, emergency states