Content deleted Content added
No edit summary |
m Expanded article, history and notable contributors with citations. |
||
Line 4:
Another current definition of industrial data processing is that it concerns those [[computer program]]s whose variables in some way represent [[Physics|physical]] quantities; for example the [[temperature]] and [[pressure]] of a tank, the position of a [[robot]] arm, etc.
== Industrial Data Processing <ref>{{Cite journal |last=Blachowicz |first=Tomasz |last2=Bysko |first2=Sara |last3=Bysko |first3=Szymon |last4=Domanowska |first4=Alina |last5=Wylezek |first5=Jacek |last6=Sokol |first6=Zbigniew |date=2025-05-24 |title=Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations |url=https://www.mdpi.com/1424-8220/25/11/3311 |journal=Sensors |language=en |volume=25 |issue=11 |pages=3311 |doi=10.3390/s25113311 |issn=1424-8220}}</ref> ==
'''Industrial data processing''' refers to the acquisition, transformation, analysis, and application of data in industrial environments such as manufacturing, energy, utilities, and logistics. It enables real-time monitoring, automation, and optimization of processes through digital systems that interface with physical machinery.
Line 10:
=== History ===
Industrial data processing emerged in the mid-20th century with the introduction of programmable logic controllers (PLCs) <ref>{{Citation |last=Bolton |first=W. |title=Programmable Logic Controllers |date=2015 |work=Programmable Logic Controllers |pages=1–22 |url=https://doi.org/10.1016/b978-0-12-802929-9.00001-7 |access-date=2025-07-19 |publisher=Elsevier |isbn=978-0-12-802929-9}}</ref>and supervisory control and data acquisition (SCADA) systems <ref>'''Boyer, S.A.,''' 2009. ''SCADA: Supervisory Control and Data Acquisition''. 4th ed. Research Triangle Park, NC: International Society of Automation (ISA).</ref>. 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 (CNC) systems and distributed control systems (DCS) advanced the field, allowing greater automation and data handling at scale<ref>{{Cite journal |last=Bell |first=R. |date=1985-01 |title=A Review of:“Computer Control of Manufacturing Systems.” By YORAM KOREN. (McGraw-Hill International Book Company, 1983.) [Pp. 287.] Price £8-95. |url=https://doi.org/10.1080/00207548508928066 |journal=International Journal of Production Research |volume=23 |issue=4 |pages=841–842 |doi=10.1080/00207548508928066 |issn=0020-7543}}</ref>. 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 <ref>{{Citation |last=Gisi |first=Philip J. |title=The Dark Factory: |date=2024-01-04 |work=The Dark Factory and the Future of Manufacturing |pages=3–19 |url=https://doi.org/10.4324/9781032688152-2 |access-date=2025-07-19 |place=New York |publisher=Productivity Press |isbn=978-1-032-68815-2}}</ref>.
=== 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.▼
▲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.▼
▲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.▼
▲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. ▼
▲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
▲Advanced analytics platforms use statistical models, artificial intelligence, and machine learning to analyze datasets in real time or retrospectively. Applications include condition-based monitoring, process optimization, automated quality assurance, and digital twin modeling.
=== Applications ===
Industrial data processing is central to:
* Smart manufacturing and Industry 4.0<ref>{{Cite web |title=Factory of the Future {{!}} Advanced Manufacturing Innovation Centre {{!}} Queen's University Belfast |url=https://we-are-amic.com/about/factory-of-the-future/ |access-date=2025-07-19 |website=we-are-amic.com}}</ref>
* Predictive maintenance and asset management
* Industrial energy efficiency and monitoring
| |||