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

• Autorzy: Iaremko Iaroslav , Senkerik Roman , Jasek Roman , Lukastik Petr

Identyfikatory

DOI

10.34768/amcs-2021-0041

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

OPC UA; OPC tree; principal component analysis; PCA; big data analysis; data reduction; machine tool; anomaly detection; emergency states

PL

OPC UA; PCA; analiza głównych składowych; zbiór danych; redukcja danych; wykrywanie anomalii; stan nadzwyczajny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 4
• Strony: 601--611
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Iaremko Iaroslav

• Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín, nám. T.G. Masaryka 5555, 760 01 Zlín, Czech Republic

autor

Senkerik Roman

• Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín, nám. T.G. Masaryka 5555, 760 01 Zlín, Czech Republic

autor

Jasek Roman

• Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlín, nám. T.G. Masaryka 5555, 760 01 Zlín, Czech Republic

autor

Lukastik Petr

• Tajmac-ZPS, třída 3. května 1180, 763 02 Zlín, Malenovice, Czech Republic

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).