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Design of Evaluation Classification Algorithm for Identifying Conveyor Belt Mistracking in a Continuous Transport
Gabriel Fedorko1, Vieroslav Molnar2, Beata Stehlikova1
1Faculty BERG, Technical University of Kosice, Park Komenského 14, 040 01 Kosice, Slovakia.
Sensors (Basel, Switzerland)
|June 27, 2024
Summary
Digital transformation of continuous transport systems requires analyzing data. This study confirms that measuring specific parameters can detect belt mistracking, enabling timely diagnostics and digital twin integration.
Area of Science:
- Engineering
- Industrial Systems
- Data Analysis
Background:
- Continuous transport systems require digital transformation for operational monitoring and diagnostics.
- Analyzing the informative value of measured quantities is crucial for effective digital transformation.
- Identifying undesirable operating conditions, such as belt mistracking, is essential for system reliability.
Purpose of the Study:
- To verify the hypothesis that belt mistracking in continuous transport systems can be identified through the measurement of selected parameters.
- To develop an evaluation algorithm for timely detection of belt mistracking.
- To assess the suitability of the developed algorithm for digital twin applications.
Main Methods:
- Experimental measurement of selected parameters in a continuous transport system.
- Analysis of the informative value of individual measured quantities.
- Development and verification of a belt mistracking identification algorithm.
Main Results:
- The hypothesis that selected parameter measurements can identify belt mistracking was confirmed.
- An evaluation algorithm for on-time detection of belt mistracking was created.
- The algorithm is suitable for integration into a digital twin of a continuous transport system.
Conclusions:
- Measuring specific parameters is an effective method for identifying belt mistracking in continuous transport systems.
- The developed algorithm facilitates operational diagnostics and supports the digital transformation of these systems.
- The findings contribute to enhanced monitoring and predictive maintenance strategies for continuous transport operations.
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