가상 센서 시장 : 구성 요소별, 데이터 소스 유형별, 조직 규모별, 용도별, 최종 이용 산업별 - 세계 시장 예측(2026-2032년)

The Virtual Sensors Market is projected to grow by USD 7.59 billion at a CAGR of 29.73% by 2032.

Virtual Sensors Executive Summary

Virtual sensors, also known as soft sensors, are becoming a core layer of industrial intelligence by estimating physical, chemical, and operational variables from existing machine data, process signals, and mathematical models. Instead of relying only on hardware instrumentation, organizations use virtual sensing to infer hard-to-measure conditions such as temperature distribution, pressure, flow, vibration, emissions, product quality, battery health, and equipment degradation in real time.

The market landscape is being shaped by industrial IoT, edge computing, digital twins, advanced analytics, and artificial intelligence. These technologies help manufacturers, utilities, automotive companies, energy operators, healthcare technology providers, and smart infrastructure owners reduce sensor dependency, improve asset visibility, and support predictive maintenance. As connected assets generate larger volumes of operational data, virtual sensors are increasingly positioned as a scalable approach to improve monitoring coverage without adding unnecessary hardware complexity.

Transformative Shifts in the Virtual Sensors Landscape

The virtual sensors landscape is shifting from isolated model-based estimation toward integrated, AI-enabled sensing ecosystems. Traditional approaches based on first-principles models, Kalman filters, observer models, and statistical inference remain important, but they are now being combined with machine learning, sensor fusion, and digital twin platforms to improve accuracy across dynamic operating conditions.

A major transformation is the movement of virtual sensing from central analytics environments to edge and cloud-edge architectures. This enables faster response in applications such as predictive maintenance, autonomous systems, industrial automation, energy optimization, and process control. At the same time, demand for sustainability reporting, equipment uptime, worker safety, and quality assurance is pushing enterprises to use virtual sensors to monitor parameters that are expensive, unsafe, or impractical to measure with physical sensors alone.

Cumulative Impact of Artificial Intelligence on Virtual Sensors

Artificial intelligence is expanding the value of virtual sensors by improving model adaptability, anomaly detection, and real-time decision support. AI-driven virtual sensors can learn relationships among multiple data streams, detect nonlinear process behavior, and estimate variables even when direct measurements are unavailable, delayed, or unreliable. This is especially valuable in complex environments such as chemical processing, automotive powertrains, battery systems, wind turbines, smart buildings, semiconductor manufacturing, and pharmaceutical production.

The cumulative impact of AI is strongest when virtual sensors are connected to digital twins and industrial control systems. Machine learning models, physics-informed AI, and hybrid analytics can enhance predictive maintenance, optimize energy consumption, improve process stability, and support closed-loop automation. However, industry leaders must address model governance, explainability, cybersecurity, data quality, calibration drift, and validation requirements to ensure virtual sensors deliver reliable insights in regulated and safety-critical operations.

Key Regional Insights for Virtual Sensors

Asia-Pacific is a high-momentum environment for virtual sensors due to large-scale manufacturing, smart factory investments, automotive electrification, electronics production, and expanding industrial IoT adoption. China, Japan, South Korea, India, and Australia are important demand centers as enterprises use soft sensing for process optimization, predictive maintenance, energy management, emissions monitoring, and quality control across discrete and process industries.

North America remains a leading innovation hub, supported by strong adoption of industrial analytics, cloud platforms, digital twins, and AI-based automation in the United States and Canada. The region's mature ecosystem for advanced manufacturing, autonomous mobility, energy infrastructure, and asset performance management supports early deployment of virtual sensor technology in mission-critical operations. Latin America is gradually increasing deployment in mining, oil and gas, utilities, food processing, and manufacturing, with Brazil and Mexico showing demand for cost-efficient monitoring, equipment reliability, and asset optimization.

Europe is driven by advanced manufacturing, automotive engineering, emissions compliance, energy efficiency, and Industry 4.0 programs across Germany, France, Italy, Spain, and the United Kingdom. The Middle East is adopting virtual sensors in oil and gas, petrochemicals, desalination, utilities, and smart infrastructure, where real-time monitoring and operational resilience are strategic priorities. Africa is seeing emerging opportunities in mining, energy access, water systems, and industrial modernization, where virtual sensing can reduce reliance on expensive physical instrumentation while improving visibility across remote assets.

Key Group Insights for Virtual Sensors

ASEAN economies are creating demand for virtual sensors through electronics manufacturing, automotive supply chains, energy systems, and smart city development. The region's cost-sensitive industrial base makes soft sensors attractive for expanding monitoring capabilities without heavy instrumentation spending, particularly in facilities seeking better quality control, process stability, and equipment uptime.

The GCC is applying virtual sensors in oil and gas, refining, petrochemicals, power generation, water treatment, and smart infrastructure, where remote monitoring, asset integrity, and operational efficiency are strategic priorities. The European Union benefits from strong regulatory focus on energy efficiency, emissions reduction, industrial data spaces, circular economy objectives, and digital transformation, supporting advanced adoption in manufacturing, clean technology, and critical infrastructure sectors.

BRICS economies combine large industrial bases with rising investment in AI, automation, mobility, energy systems, and infrastructure, creating significant long-term opportunities for virtual sensing. G7 markets lead in high-value use cases such as automotive systems, aerospace, advanced manufacturing, pharmaceuticals, semiconductors, and grid modernization. NATO-aligned economies also show relevance for secure sensing, condition monitoring, defense-adjacent manufacturing, and resilient infrastructure applications, particularly where reliability, interoperability, and cybersecurity are mission-critical.

Key Country Insights for Virtual Sensors

The United States leads in AI-enabled virtual sensors, industrial software, digital twins, cloud analytics, automotive innovation, aerospace systems, and advanced manufacturing. Canada shows strong demand in energy, mining, utilities, transportation, and clean technology, while Mexico benefits from manufacturing nearshoring, automotive production, electronics assembly, and industrial automation. Brazil is advancing use cases in mining, oil and gas, agriculture processing, pulp and paper, and energy infrastructure.

In Europe, the United Kingdom is focused on industrial digitalization, energy systems, smart infrastructure, and research-led innovation. Germany is a major center for Industry 4.0, automotive engineering, machinery, robotics, and process automation. France is advancing virtual sensing in aerospace, energy, transport, nuclear operations, and manufacturing, while Italy and Spain show opportunities in machinery, automotive components, utilities, food processing, and industrial efficiency. Russia's demand is linked to energy, mining, chemicals, and heavy industry, though technology access and geopolitical conditions can influence adoption patterns.

China is scaling virtual sensors through smart manufacturing, electric vehicles, electronics, industrial AI programs, renewable energy, and large-scale infrastructure modernization. India is expanding adoption in manufacturing, power, chemicals, mobility, pharmaceuticals, and infrastructure as enterprises modernize operations and improve asset reliability. Japan and South Korea are strong in automotive, robotics, semiconductors, electronics, shipbuilding, and precision manufacturing, where high-quality data and automation maturity support advanced soft sensing. Australia's opportunities are concentrated in mining, energy, water management, utilities, and remote industrial asset monitoring.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize high-value use cases where virtual sensors solve measurable operational problems, such as reducing downtime, estimating hard-to-measure variables, improving product quality, optimizing energy consumption, strengthening safety, and extending asset life. The strongest deployments typically begin with a clear business case, reliable historical data, and defined performance benchmarks for model accuracy, latency, robustness, and maintainability.

Organizations should combine physics-based models with machine learning to improve robustness across changing operating conditions. They should also invest in data governance, cybersecurity, model validation, drift monitoring, edge deployment readiness, and integration with existing SCADA, MES, ERP, digital twin, and asset performance management systems. Partnerships with automation specialists, cloud providers, domain experts, standards bodies, and research institutions can accelerate deployment while reducing implementation risk.

Research Methodology

This executive summary is developed using a structured research approach that synthesizes verified industry knowledge across industrial IoT, AI, digital twins, predictive maintenance, automation, process control, smart infrastructure, and connected asset management. The methodology emphasizes data-backed interpretation from established technology adoption patterns, sector-specific use cases, regulatory direction, and regional industrial capabilities.

The analysis considers demand drivers, technology enablers, deployment barriers, competitive dynamics, and end-use applications across major regions, economic groups, and priority countries. Insights are evaluated through the lens of operational value, technical feasibility, cybersecurity exposure, regulatory context, and enterprise readiness to provide decision-useful guidance for stakeholders assessing virtual sensor strategies.

Conclusion

Virtual sensors are evolving into a strategic capability for data-driven operations, enabling organizations to estimate critical variables, enhance asset performance, and scale real-time monitoring beyond the limits of physical instrumentation. Their role is expanding as industries pursue predictive maintenance, digital twins, industrial AI, sustainability goals, resilient infrastructure, and autonomous decision-making.

The strongest opportunities will emerge where virtual sensors are embedded into enterprise workflows and validated against operational outcomes. Organizations that combine domain expertise, trustworthy data, AI governance, cybersecurity, and scalable deployment architectures will be best positioned to capture the long-term value of virtual sensing across industrial, infrastructure, mobility, healthcare technology, and energy ecosystems.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. Market Share Analysis, 2025
• 3.5. FPNV Positioning Matrix, 2025
• 3.6. New Revenue Opportunities
• 3.7. Next-Generation Business Models
• 3.8. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Virtual Sensors Market, by Component

• 7.1. Model-Based Sensors
• 7.2. Data-Driven Sensors
• 7.3. Digital Twin Sensors
• 7.4. IoT-Integrated Sensors

8. Virtual Sensors Market, by Data Source Type

• 8.1. Physical Sensor-Driven Inputs
• 8.2. System Log-Based Inputs
• 8.3. Environmental Data Inputs

9. Virtual Sensors Market, by Organization Size

• 9.1. Large Enterprises
• 9.2. Small & Medium Enterprises

10. Virtual Sensors Market, by Application

• 10.1. Asset Tracking
• 10.2. Environmental Monitoring
• 10.3. Predictive Maintenance
• 10.4. Smart Building

11. Virtual Sensors Market, by End-Use Industry

• 11.1. Automotive
  • 11.1.1. Connected Vehicles
  • 11.1.2. Fleet Management
• 11.2. Energy & Utilities
  • 11.2.1. Pipeline Monitoring
  • 11.2.2. Smart Grid Monitoring
• 11.3. Healthcare
  • 11.3.1. Hospital Equipment Monitoring
  • 11.3.2. Patient Monitoring
• 11.4. Manufacturing

12. Virtual Sensors Market, by Region

• 12.1. Asia-Pacific
• 12.2. North America
• 12.3. Latin America
• 12.4. Europe
• 12.5. Middle East
• 12.6. Africa

13. Virtual Sensors Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Virtual Sensors Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Concentration Analysis, 2025
  • 15.1.1. Concentration Ratio (CR)
  • 15.1.2. Herfindahl Hirschman Index (HHI)
• 15.2. Recent Developments & Impact Analysis, 2025
• 15.3. Product Portfolio Analysis, 2025
• 15.4. Benchmarking Analysis, 2025

16. Company Profiles

• 16.1. ABB Ltd.
• 16.2. Aspen Technology, Inc.
• 16.3. C3.ai
• 16.4. Elliptic Laboratories A/S
• 16.5. Elliptic Labs
• 16.6. Emerson Electric Co.
• 16.7. Exosite LLC
• 16.8. General Electric Company
• 16.9. Honeywell International Inc.
• 16.10. Infineon Technologies AG
• 16.11. Intellidynamics
• 16.12. International Business Machines Corporation
• 16.13. Johnson Controls International plc
• 16.14. KONUX GmbH
• 16.15. Microsoft Corporation
• 16.16. Mitsubishi Electric Corporation
• 16.17. PTC Inc.
• 16.18. Radix IoT, LLC
• 16.19. Robert Bosch GmbH
• 16.20. Rockwell Automation, Inc.
• 16.21. Schneider Electric SE
• 16.22. Seeq Corporation
• 16.23. Siemens AG
• 16.24. STMicroelectronics N.V.
• 16.25. Yokogawa Electric Corporation