Why Industrial Sensors Are Becoming Smart
Just ten years ago, an industrial sensor performed a single function: it measured a physical quantity and transmitted an analog signal to a controller. Temperature, pressure, level, flow — each parameter was served by a separate device with a 4-20 mA output, and all processing logic was handled by a programmable logic controller (PLC). Today, the situation has fundamentally changed. Smart sensors do not just measure — they process data, filter noise, perform self-diagnostics and transmit information via digital protocols. This is a fundamental shift that transforms every industrial sensor into a full-fledged node of the Industrial Internet of Things (IIoT).
According to analysts, the industrial sensors market will grow from USD 30 billion in 2026 to USD 47 billion by 2031. The main growth drivers are the adoption of edge computing with AI elements and stricter energy efficiency requirements in manufacturing.
Traditional vs Smart Sensors: Key Differences
To understand the scale of change, it is worth comparing traditional analog sensors with their smart counterparts. The difference is evident in every aspect — from data transmission methods to diagnostic capabilities.
| Parameter | Traditional Sensor | Smart Sensor |
|---|---|---|
| Output signal | Analog 4-20 mA or 0-10 V | Digital (IO-Link, HART, Ethernet/IP) |
| Data processing | External, on the controller | Built-in, on the sensor microprocessor |
| Self-diagnostics | None | Continuous, with automatic notifications |
| Number of measured parameters | One | Several simultaneously |
| Noise filtering | Manual or on the controller | Automatic, algorithmic |
| Remote configuration | Physical access only | Via protocol or wirelessly |
| Additional data transmission | Not possible | Housing temperature, operating hours, status |
| IIoT integration | Requires additional gateway | Native support |
| Implementation cost | Lower initial | Lower total (TCO) thanks to diagnostics |
As the comparison shows, a smart sensor is essentially a miniature computer capable of independently analysing measurements, filtering out anomalies and alerting operators to problems before they lead to failures.
Communication Protocols: IO-Link, HART and Industrial Ethernet
A smart sensor without a communication protocol is just a sophisticated sensor with a microprocessor. It is the communication protocols that make the sensor's intelligence accessible to the entire automation system.
IO-Link — The Next-Generation Standard
IO-Link (IEC 61131-9) is the first standardised point-to-point communication protocol for sensors and actuators that is manufacturer-independent. It operates over a standard three-wire cable, allowing existing equipment to be upgraded without replacing wiring. According to industry experts, IO-Link reduces brownfield retrofit costs by up to 60%, and commissioning time decreases dramatically.
Via IO-Link, a sensor transmits not only measured values but also diagnostic information: optics contamination level, electronics temperature, operating hours counter and error codes. An IO-Link master collects data from multiple sensors and forwards it to a PLC or cloud platform via industrial Ethernet.
HART — A Digital Layer Over Analog
The HART protocol (Highway Addressable Remote Transducer) remains the most widespread in process industries. Its uniqueness lies in the fact that a digital signal is superimposed on a standard 4-20 mA analog signal, making HART sensors compatible with any analog controller input. At the same time, the digital channel gives operators access to configuration, diagnostics and additional measurements. According to research, wired digital devices — including HART, PROFIBUS PA and Foundation Fieldbus — accounted for nearly 45% of the industrial sensor market in 2025.
Industrial Ethernet
For the most demanding applications, Ethernet sensors supporting PROFINET, EtherNet/IP or EtherCAT protocols are used. They provide maximum transmission speed and allow the automation level to be integrated directly with enterprise business systems (ERP, MES). This is particularly relevant for facilities where variable frequency drives control dozens of electric motors and data on each drive's condition must be available in real time.
Types of Smart Sensors and Their Applications
Smart sensors cover virtually the entire spectrum of physical quantities measured in manufacturing. Let us examine the main types and their capabilities.
Vibration Sensors
Vibration monitoring is perhaps the most striking example of how a smart sensor changes the approach to equipment maintenance. Modern three-axis vibration sensors with IO-Link interfaces can detect anomalies, bearing wear, rotor imbalance, insufficient lubrication and cavitation at early stages. A wide frequency range of up to 5,600 Hz allows analysis of both low-speed and high-speed equipment conditions.
A telling example is monitoring an electric motor driven by a variable frequency drive. A vibration sensor on the bearing housing registers an increase in vibration amplitude, which may indicate misalignment or imbalance. Changes in the frequency spectrum point to a specific fault: bearing wear, gear defect or structural resonance. The predictive maintenance system analyses the trend and schedules a maintenance stop in advance, avoiding unplanned downtime.
Temperature Sensors
Smart temperature sensors monitor heating of motor windings, bearings, heat exchangers and process media. Unlike conventional thermocouples or resistance thermometers, an intelligent temperature sensor can compensate for reading drift over time, detect an open circuit or short circuit in the sensing element and transmit this data to the control system without operator intervention.
Pressure Sensors
In hydraulic and pneumatic systems, smart pressure sensors track not only the current value but also pressure change dynamics, peak loads and micro-leaks. This is critically important for systems where variable frequency drives control pumps maintaining a set pressure in the circuit. The sensor can automatically correct readings depending on the ambient temperature and report when calibration is needed.
Level Sensors
Radar and ultrasonic level sensors with digital interfaces provide non-contact measurement of liquid and bulk material levels. Modern models are equipped with Bluetooth for safe remote configuration via a mobile app — operators can change parameters and monitor readings without approaching the tank, which is particularly valuable in hazardous areas.
Flow Sensors
Intelligent flow meters can measure volumetric and mass flow, temperature and medium density simultaneously. Built-in diagnostics detect gas bubbles in liquid, pipe wall deposits or flow instability, allowing timely response to changes in the process.
Predictive Maintenance: From Reaction to Forecasting
The concept of predictive maintenance is one of the main reasons for the mass adoption of smart sensors. The traditional "run it till it breaks" approach is giving way to a data-driven strategy that identifies problems at an early stage and schedules repairs at a convenient time.
A typical predictive maintenance system includes:
- Smart vibration, temperature and current sensors on critical equipment
- IO-Link masters or gateways for data collection
- A PLC or edge controller for primary processing
- A software platform with machine learning elements for trend analysis
- A notification system for rapid response
Machine learning eliminates the risk of human error in data analysis. The system automatically recognises each machine's unique baseline vibration and temperature levels and sets warning and alarm thresholds at appropriate points. When a trend approaches a critical value, the system proactively generates a maintenance request.
According to industry statistics, implementing predictive maintenance with smart sensors delivers:
- 30-50% reduction in unplanned equipment downtime
- 10-40% reduction in maintenance costs
- 20-40% extension of equipment service life
- 10-25% improvement in overall equipment effectiveness (OEE)
Integration with Variable Frequency Drives and PLCs
Smart sensors reach their full potential when paired with modern automation systems. A variable frequency drive is itself a source of valuable data — current, voltage, frequency, torque, power module temperature. When data from external smart sensors is added to this information, a complete picture of the electric drive's condition emerges.
For example, a PID controller in a variable frequency drive can receive feedback from an intelligent pressure sensor via IO-Link. The sensor not only transmits the current pressure value but also reports on the sensing element's condition, calibration needs and predicted remaining service life. This is a fundamentally different level of reliability compared to analog feedback.
Modern-generation programmable controllers have built-in IO-Link and industrial Ethernet support, allowing extended diagnostics to be read directly from sensors without additional hardware. And expansion boards for controllers enable different sensor types to be connected to a single control system.
Implementing Smart Sensors: Practical Recommendations
The transition from traditional to smart sensors does not have to happen all at once. A practical approach involves phased implementation focused on the most critical equipment.
Step 1: Audit Critical Equipment
Identify equipment whose downtime costs the most. This usually means key electric motors, pumps, compressors and fans. These should be fitted with smart sensors first.
Step 2: Choose the Protocol
If the facility already has 4-20 mA infrastructure, HART sensors will provide a gradual transition without replacing the cable network. For new installations, IO-Link is the optimal choice in terms of functionality-to-cost ratio.
Step 3: Pilot Project
Start with one or two monitoring targets. Install vibration and temperature sensors, set up data collection and alert thresholds. After three to six months, evaluate the results and decide on scaling.
Step 4: Scale Up
Based on pilot project experience, roll out the monitoring system to the remaining critical equipment. Integrate smart sensor data with the enterprise management system for complete visibility of production processes.
Smart Sensor Development Trends
Intelligent sensor technologies continue to evolve rapidly. Here are the key trends that will shape the industry in the coming years:
- Edge AI — machine learning algorithms embedded in sensors enable on-site data analysis without cloud transmission
- Energy-independent power — sensors harvesting energy from vibration, heat or light operate autonomously for years
- Wireless technologies — WirelessHART, ISA100 and 5G Industrial protocols allow sensors to be installed in hard-to-reach locations
- Digital twins — data from smart sensors feeds virtual equipment models for simulation and optimisation
- Cybersecurity — protecting industrial sensor networks from unauthorised access is becoming a priority
In 2026, leading industrial automation manufacturers continue to invest in sensor technology development. ABB is investing USD 1.4 billion in expanding its sensor analytics platform, while Siemens is committing USD 2.2 billion to manufacturing IO-Link sensors and edge AI gateways.
Smart industrial sensors are no longer a future prospect but a reality of modern manufacturing. They turn every piece of equipment into a data source, make maintenance predictable and render production processes transparent and manageable.
