IT/OT Connectivity

Industrial automation becomes truly digital when measurement data can move easily between IT and OT systems. HBK makes this possible by turning sensor data into real-time production intelligence, from machine level all the way to the cloud.

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Factory Digitalization: Two Industrial Engineers Use Tablet Computer, AI Big Data Analysis. Visualization of High-Tech Facility into 3D Rendered Neural Network. Industry 4.0 Machinery Manufacturing; smart sensors; IO-Link;

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.

Acquire trusted engineering data with industry-leading measurement accuracy.

Seamlessly integrate measurement data across engineering and enterprise systems.

Transform measurement data into actionable insights that accelerate better decisions.

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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Sensing
Signal Conditioning & Edge Processing
Hybrid Edge Architectures
Control & Integration
Digital and Enterprise

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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.

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

Machine automation components - Signal processing edge

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.

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

Machine automation components - Connectivity and integration

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

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.

Condition Monitoring CAN MD

CAN‑MD smart sensors deliver intelligent vibration monitoring with embedded processing, real-time fault detection, reduced bandwidth use, and scalable asset management.

Illustration of cloud and automation protocols

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.

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