Global Gas Utility Predictive Maintenance Market Outlook, 2031

Gas utility predictive maintenance has shifted from a reliability-engineering aspiration to a regulatory and safety obligation whose analytical basis is now prescribed in pipeline integrity rules, machinery-management standards, and critical-infrastructure cybersecurity directives. The regulatory anchor is PHMSA’s 2022 revision of anomaly response and evaluation requirements for gas transmission pipelines. Sections 192.714 and 192.933 now incorporate predicted failure pressure and strain-based criteria “to assure that anomalies are repaired before they become an immediate condition and are at or near failure,” replacing depth-only thresholds with remaining-life analysis. PHMSA’s subsequent rulemaking has continued this trajectory: a May 2025 advanced notice of proposed rulemaking drew public comment overwhelmingly favoring performance-based, engineering-focused anomaly response that prioritizes calculated predicted failure pressure, strain, and remaining life over one-dimensional thresholds, and the Gas Associations like INGAA, AGA, and GPA Midstream, supported modern anomaly evaluation. For compressors, API Recommended Practice 691, first published in June 2017, defines minimum requirements for managing health, safety, and environmental risks across the entire machinery life cycle with continuous condition monitoring positioned as the mechanism that reduces POF through early failure detection and generates the auditable compliance trail the standard demands. For pressure regulators and meters, the European Association of Manufacturers of Gas Meters, Gas Pressure Regulators and associated Safety Devices and Stations, operating as FARECOGAZ, distinguishes preventive maintenance from condition-based maintenance in its surveillance guidelines, defining condition-based maintenance as a combination of condition monitoring of significant parameters and/or testing, analysis, and ensuing maintenance actions, and allowing intervals to be multiplied by a rating factor where condition monitoring is deployed. The cybersecurity layer adds a further obligation: NIS2 treats gas transmission and distribution operators as essential entities, and a predictive maintenance model for a gas turbine may need to satisfy EU AI Act high-risk requirements, NIS2 cybersecurity obligations, REMIT transparency rules, and CSRD reporting requirements simultaneously.

The manufacturing and development base is led by asset-performance and condition-monitoring providers with gas-specific deployments. Baker Hughes’ Industrial Technology division supplies the Orbit 60 condition-monitoring system, Ranger Pro wireless monitoring systems, System 1 software, and cybersecurity enhancements, and has secured long-term multi-maintenance programme contracts covering maintenance planning and condition monitoring. Emerson integrates machinery health monitoring with its AMS asset-management portfolio, extending protection-system data into diagnostic analytics and remaining-useful-life prediction. Siemens provides COMOS and Xcelerator as part of its asset-performance and predictive-maintenance offering, and was rated exemplary in the 2025 ISG Buyers Guide for Predictive Maintenance alongside GE Vernova, IFS, and Oracle. AspenTech supplies APM and process-simulation tools used for live digital-twin monitoring, and was shortlisted as a most innovative predictive-maintenance solution. GE Vernova provides Asset Performance Management, and Hexagon supplies HxGN APM. iFactory’s predictive-maintenance platform is positioned explicitly around API 691 compliance, providing continuous condition monitoring that updates the probability-of-failure estimate in real time rather than relying on quarterly vibration surveys and annual lube-oil analysis. These participants compete on diagnostic fidelity across compressor, regulator, and pipeline failure modes, integration with EAM and work-order systems, NIS2-aligned security architecture, and the ability to extend models to hydrogen-blended and variable-composition service.

Market Dynamics

Market Drivers PHMSA’s Shift from Depth Thresholds to Predicted Failure Pressure and Remaining Life: The most powerful driver across the global gas utility predictive maintenance market is the regulatory migration from calendar-based and depth-based assessment toward engineering analysis of predicted failure pressure, strain, and remaining life. Sections 192.714 and 192.933 now require scheduling decisions that incorporate predicted failure pressure and strain-based criteria to assure anomalies are repaired before they become immediate conditions and are at or near failure. PHMSA’s 2025 ANPRM and the industry comments supporting performance-based anomaly evaluation reinforce this direction, and operators are increasingly expected to demonstrate that maintenance and repair schedules are derived from calculated remaining life rather than from generic intervals.
API 691 and the Risk-Based Machinery Management Framework for Compressors: The compressor segment is being reshaped by API 691’s risk-based framework, which defines Risk as Probability of Failure multiplied by Consequence of Failure across the machinery life cycle. The standard requires documented evidence that risks have been identified, assessed, and mitigated through design, procurement, installation, commissioning, and operation, and it covers compressors, gas turbines, gearboxes, and expanders where failure presents a credible risk of loss of containment or high-energy release. Traditional compliance relies on periodic snapshots quarterly vibration surveys, annual lube-oil analysis, and scheduled overhauls which leave long intervals of undetected degradation; a bearing that begins cracking shortly after an annual inspection can fail catastrophically before the next scheduled assessment. Continuous condition monitoring closes this gap by updating the probability-of-failure estimate in real time, enabling risk-based decisions driven by actual equipment condition rather than calendar assumptions.

Market Challenges Hydrogen’s Diagnostic Gap and the Limits of Methane-Trained Models: A persistent technical challenge in gas utility predictive maintenance arises from the introduction of hydrogen into compression and pipeline service. Hydrogen compression introduces distinct sealing, tribological, and material-compatibility challenges that become reliability-limiting under high cycling frequencies and elevated pressure ratios, and studies consistently identify compressors, seals, and rotating equipment as among the most vulnerable components due to hydrogen’s effects on materials, leakage behaviour, and wear mechanisms. AI-based vibration analytics and performance monitoring for compressors are relatively mature and readily transferable from natural-gas applications, but their effectiveness in hydrogen service depends on explicitly accounting for hydrogen-specific failure modes. Diagnostic taxonomies and alarm logic must be extended to reflect hydrogen-induced seal degradation, accelerated wear, embrittlement-related damage mechanisms, and altered dynamic response, rather than relying solely on pattern-recognition models trained on methane-dominated operating regimes. For leak detection in hydrogen-blended networks, physics-based simulation can generate representative training data to partially overcome the scarcity of real-world hydrogen leak events, and hybrid approaches combining deep learning with explainable modelling can link predictions to physically interpretable leakage and flow features.
NIS2, the AI Act, and the Governance Burden of Predictive-Maintenance Data Flows: The sensor data that makes predictive maintenance possible also creates a regulatory exposure that gas utilities cannot treat as an IT concern. NIS2 treats gas transmission and distribution operators as essential entities, and predictive maintenance models that draw data from control systems open new connections into operational technology, which NIS2 brings into scope for cybersecurity risk management and twenty-four-hour early-warning incident reporting. A predictive-maintenance model for a gas turbine may need to satisfy EU AI Act high-risk requirements for critical infrastructure, NIS2 cybersecurity obligations, REMIT transparency rules where the asset participates in energy markets, and CSRD reporting requirements for the emissions impact of maintenance decisions. Energy companies have decades of governance experience for physical systems change-management boards, HAZOP studies, safety integrity levels but these frameworks are rigorous and slow, designed for systems that change infrequently, whereas AI models that retrain weekly or respond to real-time data challenge every assumption in traditional OT governance. DNV’s 2025 Energy Industry Outlook found that seventy-two percent of energy companies identified regulatory compliance as their top barrier to AI scaling, double the rate of any other barrier, and the governance gap is therefore as much a constraint on predictive-maintenance adoption as sensor coverage or algorithm accuracy.



Market Trends • Condition-Based Maintenance Intervals and Rating-Factor Multipliers for Regulators and Meters: The market is shifting from fixed-interval overhaul schedules toward condition-based intervals with documented rating factors, particularly for pressure regulators and meters where the installed base is large and the maintenance burden is recurring. FARECOGAZ’s surveillance guidelines distinguish predetermined maintenance intervals from condition-based maintenance, defining the latter as a combination of condition monitoring of significant parameters and/or testing, analysis, and ensuing maintenance actions, with intervals multiplied by a rating factor where condition monitoring is deployed. The guidelines also require that the need for maintenance of residential pressure and meter regulators be established and documented, rather than assumed from generic schedules. This trend is reinforced by IoT-enabled pressure regulator monitoring standards under development at BSI, which specify requirements for gas pressure regulating as part of an IoT system including installation, data link, application, and security, and which position IoT monitoring as supporting early detection of potential failures.
• Digital Twins, Physics-Informed Machine Learning, and Auditable Compliance Trails: The market is converging on digital-twin and physics-informed machine-learning architectures that combine sensor data with engineering models and generate audit trails suitable for regulatory review. For hydrogen-blended pipeline transportation, predictive analytics, physics-informed machine learning, and digital twins improve flow modelling, material behaviour prediction, and fault detection, while AI-controlled optimization manages hydrogen injection, compressor control, and blending ratios to maintain consistent energy supply. PHMSA’s own rulemaking record notes that three-dimensional modelling of in-line inspection data and fracture-mechanics analysis provide a data-driven and more accurate probabilistic representation of corrosion features that was not operationally feasible before the data revolution, and that finite-element analysis now allows full-scale testing simulation through numerical modelling.

Segmentation Analysis

Predictive maintenance software dominates the global market’s recurring value because the regulatory obligations that drive adoption remaining-life calculation under PHMSA’s predicted-failure-pressure criteria, probability-of-failure estimation under API 691, and condition-based interval determination under FARECOGAZ are satisfied through the analytics and workflow layer rather than through sensors alone. • The global gas utility predictive maintenance market is segmented by solution type into predictive maintenance software, condition monitoring systems, and predictive maintenance services. Software represents the dominant recurring layer, encompassing asset-performance-management platforms, remaining-life and probability-of-failure models, diagnostic and prognostic algorithms, and integration with enterprise asset management and work-order systems.
• Software must support PHMSA’s predicted failure pressure and strain-based scheduling requirements, API 691’s Risk and the condition-based maintenance logic that FARECOGAZ defines as condition monitoring plus testing, analysis, and ensuing maintenance actions.
• iFactory’s platform is positioned explicitly around API 691 compliance, updating probability-of-failure estimates in real time and generating the auditable condition data that the standard requires, while Emerson’s AMS and Baker Hughes’ System 1 extend protection-system data into diagnostic analytics and remaining-useful-life prediction.
• Condition monitoring systems represent the capital layer, encompassing vibration, temperature, pressure, acoustic, and oil-analysis sensors, wireless monitoring systems such as Baker Hughes’ Ranger Pro, and protection-grade systems such as Orbit 60.
• Predictive maintenance services represent the growth layer, spanning model configuration, diagnostic interpretation, managed monitoring, and compliance-support services. Services are particularly relevant where utilities lack in-house data-science capability or where NIS2 and AI Act governance requires independent validation of model behaviour.

Pipelines and valves are the largest asset-type segment globally because pipeline integrity obligations are the most explicitly codified, the installed base is the most extensive, and the consequence of undetected degradation is measured in loss of containment and regulatory enforcement. • By asset type, pipelines and valves represent the dominant share, encompassing transmission and distribution pipeline segments, valve sites, and associated appurtenances. PHMSA’s §§192.714 and 192.933 requirements for predicted failure pressure and strain-based scheduling make pipeline integrity the most analytically intensive application of predictive maintenance.
• Pipeline integrity monitoring is identified as the leading application within gas infrastructure predictive analytics, leveraging continuous sensor streams fused with predictive algorithms to detect corrosion, third-party interference, and material degradation, and automated monitoring has been associated with reductions in unplanned shutdowns.
• Compressors represent the second core asset-type segment, where API 691 provides the governing risk-based framework and where continuous condition monitoring is the mechanism for real-time probability-of-failure estimation. Compressor failure modes include thrust bearing overload, labyrinth seal wear, rotor imbalance, surge margin erosion, and valve condition degradation.
• Meters and pressure regulators represent a specialised but high-volume asset-type segment, where FARECOGAZ’s condition-based maintenance framework and emerging IoT monitoring standards are reshaping maintenance intervals. The asset population is large, the per-unit value is lower than compressors, and the maintenance burden is recurring, making this segment particularly suited to analytics-driven interval optimization.
• The dominance of pipelines and valves reflects the regulatory intensity of pipeline integrity management: it is where PHMSA’s predictive criteria are most explicit, where the installed base is largest, and where failure carries the highest public-safety and environmental consequence.

Gas transmission operators are the largest end-user segment globally because they own and operate the pipeline and compressor assets subject to the most explicit predictive-maintenance obligations, and because they bear the compliance and enforcement exposure that drives investment. • By end user, gas transmission operators represent the dominant share, encompassing interstate and intrastate pipeline companies and national transmission system operators that are the primary subjects of PHMSA’s §§192.714 and 192.933 requirements and API 691’s machinery-management framework.
• Gas distribution utilities represent the second core end-user group, where predictive maintenance is driven by distribution integrity management obligations, leak-management effectiveness, and the large installed base of regulators and meters subject to condition-based maintenance frameworks.
• Gas processing operators represent a specialised end-user segment, where predictive maintenance must address variable feed composition, multi-stage compression, and integration with plant-wide safety instrumented systems, and where API 691 applies to high-risk rotating equipment.
• The dominance of transmission operators reflects the structure of the regulatory obligation: the predictive-analysis requirements attach most explicitly to transmission pipeline integrity and compressor machinery management, and transmission operators own the assets through which those obligations are expressed.

Regional Analysis

North America leads in regulatory-driven predictive-maintenance specification because PHMSA’s predicted-failure-pressure criteria and API 691 create an explicit remaining-life and risk-based machinery framework, while Europe leads in condition-based maintenance standards for regulators and NIS2-aligned AI governance, and Asia-Pacific grows with gas infrastructure expansion and digital monitoring adoption. • North America is the most prescriptive market for gas utility predictive maintenance, with §§192.714 and 192.933 incorporating predicted failure pressure and strain-based scheduling, PHMSA’s 2025 ANPRM signaling continued modernization toward performance-based anomaly evaluation, and API 691 providing the risk-based machinery-management framework for compressors. The region’s dense transmission and distribution networks, LNG terminals, and processing capacity sustain demand for remaining-life analytics, continuous compressor monitoring, and audit-ready compliance trails.
• Europe is shaped by FARECOGAZ’s condition-based maintenance framework for gas pressure regulators and meters, by NIS2’s treatment of predictive-maintenance data flows as an essential-service cyber risk, and by the EU AI Act’s high-risk classification for critical-infrastructure AI. European operators are early adopters of condition-based interval optimization and IoT-enabled regulator monitoring, and the UK’s HyDeploy, H21, and FutureGrid programmes provide staged validation data for hydrogen-specific diagnostic models.
• Asia-Pacific is a growth-driven market tied to gas infrastructure expansion, city gas distribution digitalization, and the adoption of IoT and AI-enabled monitoring. China, India, and Southeast Asia represent the largest volume opportunities, with predictive maintenance specified alongside pipeline and compressor-station construction and with condition monitoring increasingly embedded in smart-metering and SCADA modernization programmes.
• Latin America and the Middle East and Africa are project-driven markets concentrated in pipeline integrity, compressor reliability, and utility modernisation, where predictive maintenance is specified alongside asset-management and integrity programmes and where international standards and operator specifications dominate procurement.

Considered in this report
• Historic Year: 2020
• Base year: 2025
• Estimated year: 2026
• Forecast year: 2031

Aspects covered in this report
• Gas Utility Predictive Maintenance Market with its value and forecast along with its segments
• Various drivers and challenges
• Ongoing trends and developments
• Top profiled companies
• Strategic recommendations

By Solution Type
• Predictive Maintenance Software
• Condition Monitoring Systems
• Predictive Maintenance Services

By Asset Type
• Pipelines and Valves
• Compressors
• Meters and Pressure Regulators

By End-User
• Gas Transmission Operators
• Gas Distribution Utilities
• Gas Processing Operators