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This paper presents an innovative architecture for the acquisition and storage of data from industrial analog sensors, based on Programmable Logic Controllers (PLC) operating within Operational Technology (OT) networks. Examples from the automotive industry are provided, covering both process parameters and signals used in predictive maintenance. The study discusses a multilayer data acquisition architecture integrating remote I/O modules, PLCs, Supervisory Control And Data Acquisition (SCADA) systems, and data servers. The experimental section analyses the efficiency of various encoding and compression methods for measurement data (float32, int16, differential representation) using the Deflate algorithm and Huffman coding. The results demonstrate that replacing direct float32 data storage with integer-based scaling enables a reduction in memory requirements by a factor of three to nine. The proposed approach reduces the load on SCADA systems, lowers database maintenance costs, and improves the processing of signals used in predictive maintenance applications.

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Innovative PLC-Based Architecture for Acquisition and Storage of Analog Sensor Data in Industrial Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Andrzej Chmielowiec,
  • Adam Błachowicz,
  • Katarzyna Antosz

摘要

This paper presents an innovative architecture for the acquisition and storage of data from industrial analog sensors, based on Programmable Logic Controllers (PLC) operating within Operational Technology (OT) networks. Examples from the automotive industry are provided, covering both process parameters and signals used in predictive maintenance. The study discusses a multilayer data acquisition architecture integrating remote I/O modules, PLCs, Supervisory Control And Data Acquisition (SCADA) systems, and data servers. The experimental section analyses the efficiency of various encoding and compression methods for measurement data (float32, int16, differential representation) using the Deflate algorithm and Huffman coding. The results demonstrate that replacing direct float32 data storage with integer-based scaling enables a reduction in memory requirements by a factor of three to nine. The proposed approach reduces the load on SCADA systems, lowers database maintenance costs, and improves the processing of signals used in predictive maintenance applications.