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Acquire trusted engineering data with industry-leading measurement accuracy.

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Seamlessly integrate measurement data across engineering and enterprise systems.

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Transform measurement data into actionable insights that accelerate better decisions.

Seamless Integration from Sensor to Cloud

Early Industry 4.0 efforts focused mainly on connecting devices, but today’s AI, machine learning, and digital twin applications require high-quality, full-bandwidth data to reach IT systems – not just the control layer.

HBK supports this shift with edge-based measurement systems that provide parallel IT and OT connectivity, so real-time control and full-bandwidth measurement data can be used across both industrial and enterprise environments.

Traditional OT systems still receive data through industrial fieldbuses and analogue interfaces. At the same time, calculated values and full-bandwidth measurement data are sent in parallel to IT systems through open standard protocols such as MQTT, OPC-AU and openDAQ™.

This enables direct access to high-quality process data at the source. It can be used in IT environments such as cloud platforms, analytics tools, and MES systems, while also enabling real-time analysis, AI and ML model training, and digital twin applications – without disrupting control loops or adding integration complexity.

From Physical Signals to Digital Intelligence

This architecture allows real‑time production data to be used at the same time for control, monitoring, optimisation, and long-term initiatives such as AI‑driven process improvement and digital factory integration.

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Smart sensors and digital-ready devices generate high-resolution, full‑bandwidth measurement data.

  • Accurate and stable data under real production conditions
  • Suitable for both real-time control and digital applications
  • Available as analogue, digital, or smart sensors
  • Advanced Smart Sensors with Embedded Intelligence: In addition to digital-ready sensors, HBK integrates next-generation smart sensors such as Dytran by HBK CAN‑MD accelerometers. These sensors embed signal processing directly at the sensing level. Instead of transmitting raw vibration data, they calculate condition indicators (e.g. RMS, spectral features) inside the sensor and communicate only relevant information over CAN-based networks. This enables ultra-efficient data usage, reduced bandwidth requirements, and decentralised diagnostics directly at the machine level.
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Edge electronics convert raw sensor signals into structured, real-time process data at the source.

  • Local filtering, compensation, and calibration
  • Built-in edge intelligence for calculations and data structuring
  • Parallel preparation of OT-ready and IT-ready data
  • Less dependence on PLCs for signal processing
  • Continuous monitoring of sensor and signal health

While edge systems provide full-bandwidth data and advanced processing, certain applications benefit from distributed intelligence inside the sensors themselves.

CAN‑MD-based smart sensors perform local data reduction and diagnostics before data reaches the edge layer. This approach complements traditional edge processing by optimising bandwidth usage and enabling scalable monitoring architectures, especially in distributed or mobile systems.

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Open interfaces connect measurement data directly into industrial control systems.

  • Native links to PLC, DCS, and SCADA
  • Support for industrial Ethernet and IO‑Link
  • Deterministic real‑time control with structured data access
  • Parallel IT and OT data flows from one measurement source
  • Scalable integration across machines, production lines, and sites

Measurement data extends into IT systems for visibility and optimisation.

  • Direct integration with MES, analytics tools, and cloud platforms
  • Access to full‑bandwidth data beyond the control layer
  • Supports real‑time analysis, AI/ML training, and digital twins
  • Enables transparency, traceability, and continuous improvement

This architecture allows real‑time production data to be used at the same time for control, monitoring, optimisation, and long-term initiatives such as AI‑driven process improvement and digital factory integration.

Key Areas of Application

Parallel Process Analysis

Evaluate multiple production processes at the same time to compare performance, identify inefficiencies, and improve decision making and overall production outcomes.

IT/Cloud Integration

Extend measurement data from the shop floor into IT systems for real‑time visibility, analytics, and connected manufacturing workflows from sensor to cloud.

AI/ML Model Training

Use high‑quality, full‑bandwidth measurement data to train AI and machine learning models for better monitoring and prediction.

Digital Twin Solutions

Provide digital twins with real, measurement data for accurate simulation, validation, and continuous optimisation.

Distributed Condition Monitoring (CAN MD)

Deploy intelligent vibration monitoring across machines and structures using CAN‑MD smart sensors.

By embedding signal processing directly in the sensor, this approach reduces data traffic while enabling real-time condition indicators and fault detection across distributed assets such as vehicles, rotating machinery, and infrastructure.

Ideal for applications where bandwidth, scalability, or system complexity are key constraints.

Related Products

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Choosing the right protocols for industrial connectivity

MQTT, REST or OPC-UA The protocol you choose shapes how effectively machines, sensors and IT systems work together. This white paper explains the strengths of each—and how to combine them for secure, scalable IT/OT connectivity from the shop floor to the cloud.

FAQs - IT/OT Connectivity

IT/OT connectivity means linking operational technology (OT) – like sensors and PLCs – with information technology (IT) systems such as cloud platforms, MES and analytics tools. It allows real-time measurement and production data to flow from machines into IT environments to support digitalisation and better decision-making.

IT/OT integration is essential for Industry 4.0 because it lets production data flow beyond control systems and into IT tools used for analytics, optimisation and automation. Without this connection, manufacturers cannot fully use real-time data, AI, machine learning or digital twin technologies.

OT data is typically used for real-time machine and process control, such as PLC signals. IT data is used for analysis, reporting, optimisation and business decision making. Modern industrial connectivity makes it possible for the same measurement data to serve both purposes at the same time.

Edge-to-cloud connectivity processes data locally at the machine (the edge) or system level before sending selected data to cloud or enterprise systems. This reduces latency, supports real-time decisions, and allows full-bandwidth measurement data to be used for analytics, AI and monitoring without overloading central systems.

Sensor-to-cloud integration gives direct access to high-quality measurement data from the source. This supports real-time monitoring, predictive maintenance and process optimisation, while also reducing integration complexity and improving data consistency across the organisation.

Industrial data can be standardised by structuring it at the source and using open communication protocols such as MQTT or OPC UA. This ensures consistent data formats across machines, simplifies system integration, and supports scalable digital architectures across multiple production sites.

IT/OT connectivity enables digital twins and AI by providing continuous access to high-quality, real-time measurement data. This data is essential for running accurate simulations, training machine learning models, and improving process performance, all without interrupting production.