Global Structural Health Monitoring Sensors for Industrial Assets Market Size, Share, Trends and Forecasts 2032

Key Findings

• The structural health monitoring (SHM) sensors for industrial assets market focuses on sensing systems that detect stress, fatigue, vibration, corrosion, cracks, and deformation to prevent failures in critical equipment and infrastructure.

• SHM deployments span industrial plants, refineries, pipelines, storage tanks, heavy machinery, mining equipment, bridges, ports, wind structures, and large rotating assets.

• Core sensor types include strain gauges, fiber Bragg grating (FBG) sensors, acoustic emission sensors, vibration/accelerometers, ultrasonic sensors, corrosion sensors, and displacement sensors.

• Adoption is driven by aging asset bases, stricter safety compliance, and rising downtime costs across process and heavy industries.

• Integration with IIoT platforms and edge analytics enables continuous condition monitoring and predictive maintenance.

• Wireless sensor networks reduce installation complexity and support monitoring across remote or hazardous locations.

• Digital twins and physics-informed models are increasingly paired with SHM data to improve failure prediction accuracy.

• Demand is strongest in high-risk environments where asset integrity directly impacts safety, environmental exposure, and production continuity.

Structural Health Monitoring Sensors for Industrial Assets Market Size and Forecast

The global structural health monitoring sensors for industrial assets market was valued at USD 6.7 billion in 2025 and is projected to reach USD 15.3 billion by 2032, growing at a CAGR of 12.6%. Growth is driven by industrial operators shifting from periodic inspection to continuous monitoring for high-value and safety-critical assets.

Aging infrastructure in oil & gas, utilities, and heavy manufacturing increases failure risk and monitoring spend. Unplanned downtime costs and production losses justify sensor investments with fast payback. Expansion of IIoT connectivity and ruggedized edge compute improves real-time analytics in harsh environments. Regulatory pressure for integrity management and audit readiness accelerates adoption. Wireless and low-power sensing architectures broaden deployment scale. Service and analytics subscriptions add recurring revenue beyond hardware.

Market Overview

Structural health monitoring sensors for industrial assets are deployed to detect early signs of structural degradation and operational stress that can lead to failures, leaks, or catastrophic incidents. These sensors capture parameters such as strain, vibration, acoustic emissions, temperature, displacement, and corrosion progression across industrial equipment and infrastructure. SHM systems typically combine sensor hardware with data acquisition units, communications, analytics software, and integrity models to translate signals into actionable maintenance decisions.

Solutions are used on both static assets such as tanks, piping, and supports and dynamic assets such as turbines, compressors, cranes, and heavy machinery frames. In harsh industrial environments, sensors require rugged packaging, EMI immunity, and long-life power strategies. Data is increasingly processed at the edge to reduce latency and bandwidth needs and to enable near-real-time alarms. Adoption aligns with predictive maintenance, risk-based inspection, and digital transformation programs across industrial operations.

Structural Health Monitoring Sensors for Industrial Assets Value Chain & Margin Distribution

Structural Health Monitoring Sensors Market By Monitoring Modality Intensity

Structural Health Monitoring Sensors – Adoption Readiness & Risk Matrix

Future Outlook

The SHM sensors market for industrial assets is expected to expand as operators prioritize asset integrity and shift toward continuous monitoring strategies that reduce risk and improve uptime. Digitalization of maintenance and integrity programs will increase demand for connected sensors and edge analytics.

Deployment will scale beyond flagship assets toward broader fleets as wireless sensor economics improve. Integration with digital twins and physics-informed reliability models will enhance prediction accuracy and reduce false alarms. Regulatory expectations for integrity documentation will reinforce monitoring adoption in critical infrastructure sectors. Vendors will develop more rugged, low-power sensors and simplified installation methods to accelerate deployment. Long-term growth is tied to aging infrastructure, rising safety expectations, and the operational value of predictive maintenance.

Structural Health Monitoring Sensors for Industrial Assets Market Trends

• Shift From Periodic Inspection To Continuous Integrity Monitoring
  Industrial operators are moving away from manual, periodic inspection cycles toward continuous monitoring that provides real-time integrity insight and earlier detection of degradation. Continuous SHM reduces the risk of missing fast-developing faults between inspection intervals. This shift is driven by increasing downtime costs and the operational value of early-warning alarms. Sensor data supports risk-based inspection programs by prioritizing maintenance where degradation signals are strongest. Continuous monitoring also improves audit readiness by creating traceable integrity records. The trend is especially strong in oil & gas, utilities, and high-value process industries. Vendors are packaging SHM as end-to-end solutions combining sensors, analytics, and services. Continuous integrity monitoring is becoming a standard reliability strategy.

• Expansion Of Fiber Optic And Distributed Sensing Architectures
  Fiber optic sensing, including FBG and distributed acoustic/temperature sensing, is expanding due to EMI immunity, long-range coverage, and suitability for hazardous environments. Distributed architectures can monitor long pipeline segments, large structures, and wide-area assets with fewer sensing nodes. Fiber solutions provide high sensitivity to strain and temperature changes that correlate with structural stress and degradation. Adoption grows in remote assets where cabling and maintenance are challenging. Integration with edge gateways enables local anomaly detection and alarm triggering. The trend is driven by demand for scalable monitoring of large industrial footprints. Fiber sensing also supports multi-parameter monitoring through single networks. Distributed sensing is emerging as a high-value trend for critical assets.

• Integration Of SHM With IIoT Platforms And Edge Analytics
  SHM systems are increasingly integrated into broader IIoT platforms that centralize asset data and enable predictive maintenance workflows. Edge analytics reduces bandwidth load and supports near-real-time alarms in low-connectivity sites. Local processing improves resilience by maintaining monitoring even during network outages. Integration enables correlation with process parameters such as pressure, flow, and temperature for improved root-cause analysis. Vendors are offering API-driven connectivity to CMMS, EAM, and digital twin platforms. This trend increases the operational value of SHM beyond standalone monitoring. Standardized dashboards and alert management improve usability for operations teams. IIoT integration is accelerating adoption across fleets.

• Growing Adoption Of Wireless Sensor Networks For Remote And Hazardous Assets
  Wireless SHM sensor networks are expanding due to reduced installation cost and the ability to deploy across remote, elevated, or hazardous locations without extensive cabling. Low-power wireless protocols and mesh networking improve coverage in complex industrial sites. Battery and energy-harvesting options support long-lived deployments. Wireless solutions enable faster retrofit programs for aging infrastructure. Operators prefer wireless for assets where shutdown windows are short and installation access is limited. Reliability and cybersecurity are being strengthened to meet industrial requirements. Wireless deployments are increasingly paired with edge gateways for local processing. Wireless SHM is becoming a key scalability pathway.

• Use Of AI And Physics-Informed Models For Failure Prediction
  AI-driven analytics and physics-informed models are being applied to SHM data to predict failure risk and remaining useful life with higher accuracy. Machine learning improves anomaly detection by learning normal operating baselines for each asset type. Physics-informed approaches help prevent false positives by constraining predictions with structural and material behavior rules. Model ensembles improve robustness across varying load conditions and environments. Predictive outputs support maintenance planning and spare parts optimization. This trend increases the economic value of SHM by shifting from detection to prediction. Vendors differentiate through model accuracy and explainability. Predictive analytics is shaping the next stage of SHM adoption.

Market Growth Drivers

• Aging Industrial Infrastructure And Rising Failure Risk
  Aging pipelines, tanks, rotating assets, and industrial structures face increased fatigue, corrosion, and degradation risk, making integrity monitoring a priority. Many industries operate assets beyond original design life due to replacement costs. SHM sensors provide early warning signals that reduce failure probability and support life-extension strategies. As infrastructure ages, inspection frequency and monitoring budgets increase. Failure risk carries safety, environmental, and reputational consequences. Operators justify sensor investments through avoided incidents and reduced downtime. Aging infrastructure creates sustained, long-term demand for SHM deployments. This driver is a core structural growth factor.

• High Cost Of Unplanned Downtime And Production Losses
  Unplanned downtime in process industries and heavy manufacturing can create substantial production losses, equipment damage, and supply disruption. SHM sensors reduce downtime by detecting degradation before failure and enabling planned maintenance. Early detection prevents secondary damage and improves repair scheduling. Predictive maintenance improves asset availability and throughput. Downtime costs are increasing as industrial systems become more interconnected and capacity utilization rises. Operators allocate budgets to reliability solutions with measurable ROI. SHM adoption is strengthened by quantifiable downtime avoidance economics. Downtime cost pressure is a major market accelerator.

• Stricter Safety, Environmental, And Compliance Requirements
  Regulatory frameworks and corporate safety programs increasingly require integrity management for critical assets such as pressure vessels, pipelines, and storage systems. Continuous monitoring supports compliance by providing documented evidence of asset condition and response actions. Environmental risk from leaks or structural failures increases pressure for proactive monitoring. Audits and reporting expectations encourage adoption of standardized SHM systems. Compliance mandates also influence insurance and liability considerations. Operators invest in SHM to reduce incident probability and demonstrate due diligence. Safety and compliance requirements drive adoption across critical infrastructure sectors. Regulation-backed integrity focus is a strong driver.

• Industrial Digitalization And Predictive Maintenance Programs
  Digital transformation programs are expanding across industrial operations, creating demand for connected sensors and analytics that enable predictive maintenance. SHM data becomes a key input to asset health dashboards and maintenance planning systems. Integration with CMMS and EAM platforms increases actionability. Digital twins require real-world sensing data to calibrate structural behavior models. Industry 4.0 adoption increases connectivity and data utilization readiness. SHM aligns with strategic initiatives focused on reliability and efficiency. Digital programs provide budget and organizational support for SHM deployments. Digitalization enables faster market penetration.

• Improved Sensor Ruggedization And Deployment Economics
  Advances in sensor packaging, wireless power strategies, and modular installation methods are reducing deployment barriers and expanding feasible use cases. Rugged sensors with longer calibration stability lower maintenance burden. Wireless and energy harvesting reduce cabling costs and improve retrofit feasibility. Lower total cost of ownership improves ROI for mid-tier assets, expanding market scope beyond flagship infrastructure. Standardized kits and managed services reduce project complexity. Improved economics increase adoption across broader asset fleets. Deployment improvements support scaling from pilot projects to enterprise rollouts. Better sensor economics is a strong enabling driver.

Challenges in the Market

• Signal Noise, False Alarms, And Data Quality Issues
  Industrial environments generate significant vibration, electromagnetic interference, and operational noise that can obscure true degradation signals. Poor signal-to-noise ratio increases false alarms and undermines trust in SHM systems. Baseline drift due to temperature changes or mounting variation complicates interpretation. Data gaps from connectivity issues reduce monitoring reliability. High false-positive rates increase workload for reliability teams and can lead to alarm fatigue. Accurate feature extraction and model tuning are required to maintain performance. Data quality management becomes more complex in large deployments. Ensuring reliable, actionable signals remains a central challenge.

• Installation Complexity And Access Constraints On Industrial Assets
  Many critical assets are difficult to access due to elevation, hazardous zones, insulation layers, or operational constraints, increasing installation cost and complexity. Shutdown windows may be limited, making sensor mounting difficult without disrupting production. Cabling routes can be challenging in dense industrial sites. Surface preparation and mounting quality affect long-term signal stability. Installation often requires specialized technicians and safety procedures. These constraints increase project timelines and cost. Operators may delay deployments due to access and downtime concerns. Installation friction limits rapid scaling.

• Integration With Legacy Systems And Heterogeneous Data Environments
  Industrial operators often use multiple legacy monitoring systems, CMMS platforms, and data historians, complicating SHM integration. Lack of standardized data models increases engineering effort. Cybersecurity and network segmentation policies can block sensor connectivity. Interoperability issues reduce the speed of enterprise rollout. Data governance and ownership challenges arise when multiple teams share responsibility. Integrating SHM data into existing workflows is essential for actionability. Without integration, monitoring can remain siloed and underutilized. Legacy integration complexity is a persistent adoption barrier.

• Power Management And Long-Term Maintenance Burden
  Wireless sensors require robust power strategies, and battery replacement in hazardous or remote sites is costly and operationally disruptive. Energy harvesting approaches can be inconsistent depending on vibration and thermal availability. Long-term calibration stability is required to avoid frequent maintenance visits. Harsh conditions can degrade connectors, seals, and sensor accuracy over time. Maintenance burden increases total cost of ownership and affects ROI. Operators demand multi-year reliability with minimal intervention. Achieving long-life deployments remains challenging. Power and maintenance constraints influence adoption decisions.

• Proving ROI And Scaling Beyond Pilot Projects
  Many SHM deployments begin as pilot projects on a small number of assets, and scaling enterprise-wide requires clear ROI proof and operational integration. Benefits may be probabilistic, centered on avoided failures that are difficult to quantify. Decision-makers require business cases that link monitoring to measurable downtime reduction and risk mitigation. Without clear workflows for response and maintenance, sensor data may not translate into value. Scaling also requires organizational change in maintenance planning and reliability culture. Budget competition with other capex projects can slow rollout. Demonstrating repeatable value at scale remains a key challenge.

Structural Health Monitoring Sensors for Industrial Assets Market Segmentation

By Sensor Type

• Strain Gauges & Load Sensors

• Vibration & Accelerometer Sensors

• Acoustic Emission Sensors

• Ultrasonic & Thickness Sensors

• Fiber Optic (FBG/Distributed) Sensors

By Asset Type

• Pipelines & Storage Tanks

• Rotating Equipment (Turbines, Compressors)

• Heavy Machinery & Industrial Frames

• Structures (Bridges, Ports, Industrial Buildings)

• Energy Assets (Wind Structures, Power Plant Components)

By End User Industry

• Oil & Gas

• Power & Utilities

• Manufacturing & Process Industries

• Mining & Metals

• Transportation & Infrastructure Operators

By Region

• North America

• Europe

• Asia-Pacific

• Latin America

• Middle East & Africa

Leading Key Players

• Honeywell International

• Emerson Electric Co.

• Siemens AG

• GE Vernova

• Baker Hughes Company

• Hexagon AB

• National Instruments (NI)

• Safran S.A.

• SKF Group

• Teledyne Technologies

Recent Developments

• Honeywell expanded industrial sensing suites supporting connected integrity monitoring in harsh environments.

• Emerson enhanced asset monitoring platforms integrating vibration and structural sensing analytics.

• Siemens advanced digital twin-enabled reliability workflows for large industrial assets.

• Hexagon strengthened industrial analytics capabilities for condition monitoring and integrity management.

• SKF improved vibration sensing and predictive maintenance toolchains for rotating industrial equipment.

This Market Report Will Answer The Following Questions

• What is the growth outlook for SHM sensors in industrial assets through 2032?

• Which sensor types and modalities drive the highest adoption?

• How do wireless deployments change SHM economics and scalability?

• What role do digital twins and predictive analytics play in SHM value creation?

• Which asset types represent the largest opportunities?

• How do regulatory and safety requirements influence adoption?

• What integration challenges exist with legacy industrial systems?

• How do operators measure ROI and scale beyond pilots?

• Who are the leading players and where are they differentiating?

• What innovations will shape next-generation SHM deployments?