Virtual Sensor
What is Virtual Sensor?
A virtual sensor, also known as a soft sensor, is a software-based system that estimates physical measurements using algorithms and existing sensor data. In Edge AI, virtual sensors provide real-time insights without requiring additional hardware, enabling faster, scalable, and cost-effective monitoring of industrial and IoT systems.
A virtual sensor predicts or calculates physical parameters by analyzing data from multiple physical sensors and models. Unlike traditional sensors, it does not measure directly but infers values using AI, machine learning, and mathematical models at the network edge.
Why Is It Used?
Virtual sensors reduce hardware dependency, lower costs, and extend monitoring capabilities to parameters that are difficult or expensive to measure directly. They enhance predictive maintenance, process optimization, and real-time decision-making in IoT and industrial environments.
How Is It Used?
Integrated with Edge AI platforms for on-device computation.
Collects data from physical sensors, edge devices, and cloud analytics.
Uses machine learning models to estimate parameters like temperature, pressure, or vibration.
Provides actionable insights for automated control, alerts, and predictive maintenance.
Types of Virtual Sensor
Model-based Virtual Sensors: Use mathematical and physics-based models.
Data-driven Virtual Sensors: Leverage machine learning algorithms for estimation.
Hybrid Virtual Sensors: Combine both model-based and data-driven approaches for enhanced accuracy.
Benefits of Virtual Sensor
Minimizes the need for physical sensors, reducing installation and maintenance costs.
Delivers real-time insights at the edge for faster decision-making.
Enhances predictive maintenance and operational efficiency.
Improves safety by monitoring parameters that are difficult or dangerous to measure directly.