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Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable

Tomasz Blachowicz1,2, Sara Bysko3, Szymon Bysko1

  • 1PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, Poland.

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|September 19, 2025
PubMed
Summary

This study introduces time-shifted maps (TSMs), a novel, interpretable method for analyzing industrial data. TSMs offer clear visualizations to detect anomalies and improve production process control in industrial automation.

Keywords:
industrial data analysismonitoring of industrial processespredictive maintenanceproduction in a robotic cell

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Area of Science:

  • Industrial Automation and Signal Processing
  • Data Analysis and Machine Learning

Background:

  • Traditional time-domain industrial signals limit direct stability assessment and anomaly detection.
  • Advancements in computing and data collection necessitate novel analysis methods for industrial applications.

Purpose of the Study:

  • Introduce time-shifted maps (TSMs) as a new technique for industrial data analysis.
  • Provide a simple, interpretable algorithm for processing data from standard industrial automation systems.
  • Facilitate enhanced monitoring and control of production processes through clear visual representations.

Main Methods:

  • TSMs are constructed from time series data acquired via an acceleration sensor on a robot base.
  • The effectiveness of TSMs is evaluated against classical methods like Fast Fourier Transform (FFT) and wavelet transform.
  • TSMs are classified using computed correlation dimensions and entropy measures.

Main Results:

  • TSMs provide clear, visual representations of industrial data, revealing hidden patterns.
  • The method demonstrates effectiveness in identifying anomalous scenarios through numerical simulations.
  • Comparison with FFT and wavelet transform validates TSM's utility in signal analysis.

Conclusions:

  • TSMs offer a novel and interpretable approach to industrial data analysis, complementing existing machine learning techniques.
  • The visual nature of TSMs aids in the monitoring and control of production processes.
  • TSMs show promise for detecting anomalies and assessing stability in industrial automation systems.