P23 - DisDAQ - A Modular and Distributed Measurement and Processing System for Industrial Process Optimization

Abstract

In the dynamic realm of industrial manufacturing, enhancing efficiency is important for optimizing resource use, minimizing operating costs, and boosting productivity.Addressing global competition, custom production demands, and sustainability goals necessitates improvements in machinery and facility efficiency and flexibility.These improvements require comprehensive information on machine states, environments, and processes, often achieved through enhanced data utilization and additional sensors.When enhancing existing sensor capabilities, in a first step, important key parameters like the required physical quantity, sensor sensitivity, sampling rate and clamping position need to be clarified.Since these parameters are often not known in advance, they need to be targeted in feasibility studies and initial measurement campaigns.This paper introduces a distributed data acquisition system, DisDAQ (Distributed Data Acquisition and Algorithms Quiver), designed for such feasibility studies and initial measurement campaigns as well as industrial process monitoring, analysis, and optimization.DisDAQ integrates data collection from a scalable array of spatially distributed sensors, supports various sensor types, and minimizes measurement campaign preparation.Leveraging the Robot Operating System (ROS) 2, DisDAQ employs containerized ROS2 nodes packaged with the package manager Helm for flexible, synchronized data acquisition and processing, facilitating edge computing to preprocess data.Three different real-world application scenarios are presented which show the flexibility and general applicability of DisDAQ for different application areas.