In the roaring workshops of modern factories, it is no longer individual advanced equipment that
drives efficiency leaps and decision-making transformations. Instead, it is the data flowing like blood
through every corner of the production line. The Industrial Automation Data Acquisition System
serves as the core infrastructure for building this “data goldmine.” Functioning like the factory's
nervous system, it precisely senses and transmits the pulse of production in real time, transforming
silent equipment status and process parameters into valuable information that is measurable,
analyzable, and actionable. This lays an irreplaceable foundation for enterprises' intelligent upgrades.
Transcending Traditional Recording: A Real-Time, Comprehensive,
Intelligent Data Perception Revolution
Traditional production data recording often relies on manual meter reading, periodic sampling, and
post-event data entry, suffering from delays, partiality, and error risks. Modern industrial automation
data acquisition systems achieve a qualitative leap:
Real-time Capture of All Elements: Through extensively deployed sensors (temperature, pressure, vibration,
flow, vision, etc.), equipment controllers (PLC, CNC, robot controllers), and smart instruments, the system
captures equipment operational status (start/stop, speed, current, alarms), process parameters (temperature
curves, pressure settings, recipe values), material consumption (batch, usage), environmental variables
(temperature/humidity, energy consumption), and critical product quality characteristics (dimensions,
weight, defect information).
Protocol Compatibility, Breaking Down Silos: Powerful data acquisition gateways (DAQ Gateways) or edge
computing nodes possess multi-protocol parsing capabilities (e.g., OPC UA, Modbus TCP/RTU, Profinet,
EtherNet/IP, MQTT, etc.), seamlessly integrates with diverse equipment and systems across brands, generations,
and interfaces within the workshop. This breaks down “information silos,” enabling unified data access
and standardization.
Edge Intelligence Pre-screening: At the data source (edge side), the system performs preliminary cleaning,
filtering, compression, and basic analysis (e.g., threshold alarms, simple statistics). This not only reduces
network bandwidth pressure but also enables real-time response to critical events (e.g., equipment shutdown
alerts, immediate intervention for process parameter deviations), enhancing operational efficiency.
Core Value: Precision Operations and Decision-Making Driven by Data
Deploying an efficient and reliable data acquisition system delivers value across all manufacturing operations:
Transparent Equipment Health Monitoring, Eliminating Reactive Maintenance:
Real-time Monitoring and Early Warning: Continuously collect critical equipment parameters (vibration spectra,
temperature trends, current harmonics). Combine with predefined rules or machine learning models to accurately
identify early-stage anomalies (e.g., initial bearing wear, minor motor imbalance). Enable predictive maintenance
(PdM), shifting from reactive shutdowns to proactive interventions, significantly reducing unplanned downtime losses.
In-depth Fault Diagnosis: Detailed operational history provides robust evidence for root cause analysis of
equipment failures, shortening repair time and enhancing repair quality.
Production Process Traceability, Precision Quality Control:
End-to-End Data Binding: Comprehensively records and binds production process parameters (time, equipment,
operator, process setpoints, actual measurements, environmental conditions) for each product/batch, enabling
full traceability. In case of quality issues, quickly pinpoint the problematic process, root cause, and impact scope.
Online Quality Monitoring:
Real-time collection of data from online inspection equipment (e.g., vision systems, laser gauges, spectrometers)
automatically determines product acceptance, instantly rejects non-conforming items, and calculates process
capability indices (CPK). This enables closed-loop quality control, significantly reducing scrap rates and customer
complaint risks.
Production Efficiency Visualization: Exposing Every Bottleneck
Precise OEE Calculation: Automatically collects critical data including equipment runtime, planned downtime,
unplanned downtime, performance speed loss time, and qualified output. Real-time, accurate calculation of
Overall Equipment Effectiveness (OEE) reveals true equipment utilization levels.
Bottleneck Workstation Identification: By comparing production cycle times and waiting times across different
lines and stations, it visually identifies bottlenecks constraining overall efficiency, providing data support for
optimizing production scheduling and balancing production lines.
Precision Energy & Material Consumption, Cost Control Down to the “Point”:
Real-Time Energy Monitoring: Collects electricity, water, and gas consumption data at the critical equipment,
production line, and even process level. Analyzes consumption patterns to identify inefficient segments and
abnormal usage (e.g., excessive standby power, air compressor leaks), providing precise guidance for energy
savings and consumption reduction.
Precision Material Management: Track consumption and flow of raw materials, auxiliary materials, and
work-in-progress. Combine production plans and output to calculate actual losses, optimize inventory
levels, and reduce waste.
Providing “Data Fuel” for Advanced Applications:
Digital Twin Foundation: Comprehensive, real-time, high-fidelity production data is essential for building
and driving Digital Twin models used in simulation optimization, virtual commissioning, employee
training, and more.
AI/ML Analysis Foundation: Massive historical and real-time data serve as the “nourishment” for training
artificial intelligence (AI) and machine learning (ML) models, enabling advanced intelligent applications
like precise quality prediction, process parameter optimization, and demand forecasting.
Key Elements for Building a Robust Data Acquisition System
Achieving efficient, stable data acquisition is no simple task. Focus on these core aspects:
Define Acquisition Goals and Scope: Avoid a scattergun approach. Prioritize core business objectives (e.g.,
improving OEE, reducing scrap rates, enabling traceability) and identify critical data sources (specific
equipment, parameters, and collection frequencies).
Selecting the Appropriate Architecture:
Edge Layer: Deploy gateways and edge computing devices responsible for protocol parsing, data
preprocessing, and real-time responses. Consider computational power, interface diversity, and
environmental adaptability.
Network Layer: Design reliable, secure industrial networks (wired Ethernet, industrial wireless like
Wi-Fi 6/private 5G) to ensure real-time and stable data transmission.
Platform Layer: Utilize an Industrial IoT (IIoT) platform or time-series database (e.g., InfluxDB, TimescaleDB)
for massive data storage, management, visualization, and interface exposure.
Ensure Data Quality and Security:
Data Governance: Establish unified naming conventions, units, and timestamp standards. Implement data
cleansing and validation rules.
Cybersecurity: Implement a layered defense system using firewalls, network gateways, VPNs, device authentication,
and data encryption to protect industrial data and production security.
Prioritizing System Openness and Scalability: Select platforms and components supporting standard interfaces
(e.g., RESTful API, MQTT) to ensure seamless integration with upper-tier systems (MES, ERP, BI) and flexible
adaptation to future device additions or evolving requirements.
Embracing a Data-Driven Future
Industrial automation data acquisition systems are no longer optional extras but essential foundations for
manufacturers advancing toward intelligent, digital, and green transformation. They render invisible production
processes transparent and measurable, shifting decision-making from “experience-based” to “data-driven”
precision management. Investing in a robust, reliable data acquisition system means investing in your factory's
“data perception capabilities.” This injects continuous momentum into unlocking efficiency potential, enhancing
product quality, optimizing operational costs, and achieving sustainable development. In this era of intelligent
manufacturing where data reigns supreme, those who first unlock the full potential of data acquisition will
seize the competitive advantage of the future. Review your current data landscape now, build a solid data
foundation, and embark on a new chapter of intelligent manufacturing.