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Paper details
Number 4 - December 2021
Volume 31 - 2021
An effective data reduction model for machine emergency state detection from big data tree topology structures
Iaroslav Iaremko, Roman Senkerik, Roman Jasek, Petr Lukastik
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.
Keywords
OPC UA, OPC tree, PCA, big data analysis, data reduction, machine tool, anomaly detection, emergency states