Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations
The rapid advancement of computing power, combined with the ability to collect vast amounts of data, has unlocked new possibilities for industrial applications. While traditional time–domain industrial signals generally do not allow for direct stability assessment or the detection of abnormal situat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3311 |
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| author | Tomasz Blachowicz Sara Bysko Szymon Bysko Alina Domanowska Jacek Wylezek Zbigniew Sokol |
| author_facet | Tomasz Blachowicz Sara Bysko Szymon Bysko Alina Domanowska Jacek Wylezek Zbigniew Sokol |
| author_sort | Tomasz Blachowicz |
| collection | DOAJ |
| description | The rapid advancement of computing power, combined with the ability to collect vast amounts of data, has unlocked new possibilities for industrial applications. While traditional time–domain industrial signals generally do not allow for direct stability assessment or the detection of abnormal situations, alternative representations can reveal hidden patterns. This paper introduces time-shifted maps (TSMs) as a novel method for analyzing industrial data—an approach that is not yet widely adopted in the field. Unlike contemporary machine learning techniques, TSM relies on a simple and interpretable algorithm designed to process data from standard industrial automation systems. By creating clear, visual representations, TSM facilitates the monitoring and control of production process. Specifically, TSMs are constructed from time series data collected by an acceleration sensor mounted on a robot base. To evaluate the effectiveness of TSM, its results are compared with those obtained using classical signal processing methods, such as the fast Fourier transform (FFT) and wavelet transform. Additionally, TSMs are classified using computed correlation dimensions and entropy measures. To further validate the method, we numerically simulate three distinct anomalous scenarios and present their corresponding TSM-based graphical representations. |
| format | Article |
| id | doaj-art-ab97ea8344cd45698bb5eaea5ad36c69 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ab97ea8344cd45698bb5eaea5ad36c692025-08-20T02:32:56ZengMDPI AGSensors1424-82202025-05-012511331110.3390/s25113311Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable SituationsTomasz Blachowicz0Sara Bysko1Szymon Bysko2Alina Domanowska3Jacek Wylezek4Zbigniew Sokol5PROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, PolandFaculty of Automatic Control, Electronics and Computer Sciense, Silesian University of Technology, Akademicka 16 Str., 44-100 Gliwice, PolandPROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, PolandInstitute of Physics—CSE, Silesian University of Technology, S. Konarskiego 22B Str., 44-100 Gliwice, PolandPROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, PolandPROPOINT S.A., R&D Department, Bojkowska 37 R Str., 44-100 Gliwice, PolandThe rapid advancement of computing power, combined with the ability to collect vast amounts of data, has unlocked new possibilities for industrial applications. While traditional time–domain industrial signals generally do not allow for direct stability assessment or the detection of abnormal situations, alternative representations can reveal hidden patterns. This paper introduces time-shifted maps (TSMs) as a novel method for analyzing industrial data—an approach that is not yet widely adopted in the field. Unlike contemporary machine learning techniques, TSM relies on a simple and interpretable algorithm designed to process data from standard industrial automation systems. By creating clear, visual representations, TSM facilitates the monitoring and control of production process. Specifically, TSMs are constructed from time series data collected by an acceleration sensor mounted on a robot base. To evaluate the effectiveness of TSM, its results are compared with those obtained using classical signal processing methods, such as the fast Fourier transform (FFT) and wavelet transform. Additionally, TSMs are classified using computed correlation dimensions and entropy measures. To further validate the method, we numerically simulate three distinct anomalous scenarios and present their corresponding TSM-based graphical representations.https://www.mdpi.com/1424-8220/25/11/3311industrial data analysismonitoring of industrial processespredictive maintenanceproduction in a robotic cell |
| spellingShingle | Tomasz Blachowicz Sara Bysko Szymon Bysko Alina Domanowska Jacek Wylezek Zbigniew Sokol Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations Sensors industrial data analysis monitoring of industrial processes predictive maintenance production in a robotic cell |
| title | Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |
| title_full | Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |
| title_fullStr | Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |
| title_full_unstemmed | Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |
| title_short | Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |
| title_sort | time shifted maps for industrial data analysis monitoring production processes and predicting undesirable situations |
| topic | industrial data analysis monitoring of industrial processes predictive maintenance production in a robotic cell |
| url | https://www.mdpi.com/1424-8220/25/11/3311 |
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