Introduction

The integration of virtual sensors and digital twins represents one of the most transformative advancements in industrial technology. Digital twins are virtual replicas of physical assets or systems, used to simulate, predict, and optimize performance. Virtual sensors act as their “nervous system,” providing data-driven estimations that feed into these twins. Together, they enable organizations to achieve real-time insights, predictive maintenance, and operational excellence.

The global digital twin market surpassed USD 17 billion in 2024, and virtual sensors are responsible for a growing portion of this ecosystem’s success. As industries adopt digital twins for smart factories, infrastructure, and transportation, virtual sensors are becoming indispensable data sources.

How Virtual Sensors Empower Digital Twins

Virtual sensors model environmental conditions and system parameters that physical sensors cannot easily measure. In digital twins, these simulated readings provide continuity and precision. For example:

• Aerospace manufacturers use virtual sensors to model engine temperature or fuel flow during extreme conditions.

• Energy utilities use them to predict turbine vibrations or grid load variations.

• Smart cities rely on them to model air quality and traffic flow without installing thousands of physical devices.

This symbiotic relationship enables cost-efficient, scalable monitoring solutions for complex, interconnected systems.

Market Drivers

1. Industry 4.0 Integration: The rise of smart factories has accelerated digital twin adoption, driving demand for virtual sensors as data enablers.

2. IoT Expansion: IoT platforms depend on continuous data streams—virtual sensors fill gaps where physical sensors are impractical.

3. AI-Enhanced Predictive Analytics: Machine learning models within digital twins use virtual sensor data to predict failures and optimize performance.

Challenges

Interoperability remains a challenge. Integrating virtual sensors into diverse digital twin architectures requires standardized frameworks and robust communication protocols. Model calibration is also crucial—errors in simulation can compromise decision-making accuracy.

Regional Trends

North America and Europe dominate due to early adoption of digital twin technologies in aerospace, automotive, and utilities. Asia-Pacific’s adoption is surging, led by industrial digitalization programs in China, Japan, and South Korea.

Key Players

Siemens, Dassault Systèmes, PTC, Ansys, and Bentley Systems are heavily investing in combined digital twin–virtual sensor platforms. Startups such as Modelway Srl and Twinify Technologies are introducing AI-based simulation models.

Outlook

By 2035, nearly 70% of digital twin deployments are expected to incorporate virtual sensors, making them an integral component of the global smart infrastructure revolution.