Industrial data processing
Industrial data processing is a branch of applied computer science that covers the area of design and programming of computerized systems which are not computers as such — often referred to as embedded systems. The products concerned contain at least one microprocessor or microcontroller, as well as couplers.
Another current definition of industrial data processing is that it concerns those computer programs whose variables in some way represent physical quantities; for example the temperature and pressure of a tank, the position of a robot arm, etc.
History
Industrial data processing emerged in the mid-20th century with the introduction of programmable logic controllers and supervisory control and data acquisition systems. These technologies allowed industrial operators to monitor and control machinery using digital inputs and outputs.During the 1970s and 1980s, the integration of computer numerical control systems and distributed control systems advanced the field, allowing greater automation and data handling at scale. The proliferation of sensors and industrial networks laid the groundwork for Industry 4.0, where cloud computing, edge processing, and artificial intelligence are increasingly embedded in industrial environments.
Components
Data Acquisition Industrial data is collected from sensors, actuators, control systems, and machines via analog and digital signals. These data streams can include temperature, pressure, vibration, speed, voltage, and other process variables.Real-Time Processing Systems such as edge computing devices, microcontrollers, and industrial PCs process data locally to minimize latency and increase reliability. Pre-processing functions may include filtering, anomaly detection, and logic-based event handling.
Storage and Archiving Data historians and time-series databases store large volumes of chronological data. These archives are essential for long-term performance monitoring, regulatory compliance, root cause analysis, and predictive maintenance.
Communication Protocols Industrial data processing relies on communication protocols such as Modbus, OPC-UA, PROFIBUS, and MQTT to transmit data between field devices, control systems, and enterprise applications.
Data Analysis and Decision Support Advanced analytics platforms use statistical models, artificial intelligence, and machine learning to analyse datasets in real time or retrospectively. Applications include condition-based monitoring, process optimization, automated quality assurance, and digital twin modelling.
Applications
Industrial data processing is central to:- Smart manufacturing and Industry 4.0
- Predictive maintenance and asset management
- Industrial energy efficiency and monitoring
- Robotics and motion control
- Infrastructure management
- Logistics and supply chain automation