Predictive Maintenance Market by Monitoring Infrastructure (Sensors & Sensing, Imaging & Inspection Devices, Edge Monitoring, Connectivity Hardware), Software (APM, IIoT, Digital Twin, AI-driven Predictive Maintenance Platforms) - Global Forecast to 2031

The predictive maintenance market is expected to grow from USD 9.71 billion in 2026 to USD 16.74 billion by 2031, growing at a CAGR of 11.5% during the forecast period. Regulatory focus on industrial safety, operational transparency, and asset reliability are accelerating the adoption of advanced predictive maintenance platforms across manufacturing, energy, and infrastructure sectors. Industrial operators are required to maintain strict equipment monitoring and maintenance documentation to ensure operational safety and compliance with reliability standards. Technology providers highlight that predictive maintenance systems leverage connected sensors, IoT networks, and advanced analytics to continuously assess equipment condition and identify early indicators of potential failure. These solutions enable organizations to transition from reactive maintenance to data-driven asset-monitoring strategies that improve equipment uptime and operational visibility. As industries expand digital transformation initiatives, companies are implementing intelligent maintenance platforms that strengthen equipment oversight, support compliance with safety practices, and enhance decision-making across large industrial environments.

Vendors in the predictive maintenance market are expanding platform capabilities by integrating artificial intelligence, industrial IoT data streams, and advanced analytics into unified asset performance management ecosystems. Leading technology providers combine machine learning models, vibration monitoring, and operational data analytics to generate accurate equipment health insights and predictive alerts. These systems enable maintenance teams to identify performance deviations, proactively schedule service activities, and coordinate maintenance operations across geographically distributed facilities. By consolidating operational intelligence within centralized maintenance platforms, organizations gain improved asset visibility, optimized maintenance planning, and enhanced production continuity. As connected industrial environments continue to evolve, predictive maintenance solutions are becoming critical for improving equipment reliability, minimizing operational disruptions, and enabling data-driven maintenance strategies across modern industrial operations.

“By vertical, the manufacturing segment is expected to dominate the market.”

Manufacturing organizations are increasingly implementing predictive maintenance technologies as industries prioritize equipment reliability, production continuity, and the optimization of asset performance across complex industrial environments. By integrating condition monitoring sensors, industrial IoT networks, and advanced analytics platforms with factory equipment, manufacturers gain continuous visibility into machine health and operational performance. These systems enable early detection of anomalies, allowing maintenance teams to schedule service activities before failures occur. This approach reduces unplanned downtime, improves asset utilization, and strengthens production efficiency across manufacturing facilities. AI-driven diagnostics and real-time operational dashboards further enhance maintenance planning and decision-making. As manufacturers accelerate digital transformation and smart factory initiatives, the adoption of predictive maintenance solutions in this end-user segment continues to grow rapidly.

“By offering, the AI-driven predictive maintenance platform is projected to grow at the highest CAGR during the forecast period.”

Organizations are increasingly deploying AI-driven predictive maintenance platform software as industries emphasize intelligent asset supervision, operational continuity, and proactive equipment servicing across complex production environments. By combining machine learning models with industrial IoT sensors, cloud infrastructure, and asset performance applications, enterprises obtain continuous insight into machine condition and operational patterns. These platforms analyze historical and streaming equipment data to detect irregular behavior, anticipate potential failures, and inform maintenance planning. The approach improves asset availability, limits unexpected disruptions, and strengthens maintenance strategy across industrial operations. As digital manufacturing and connected factory initiatives accelerate globally, the adoption of AI-enabled predictive maintenance platforms is expanding rapidly.

“North America will have the largest market share in 2026, and Asia Pacific is slated to grow at the highest rate during the forecast period.”

North America holds the leading share of the predictive maintenance market due to strong adoption of industrial IoT technologies, advanced analytics platforms, and mature digital infrastructure across key industries. Enterprises across the US and Canada increasingly deploy AI-enabled maintenance solutions to monitor equipment performance, detect anomalies, and prevent unexpected operational disruptions. Industrial operators are integrating connected sensors, cloud platforms, and machine learning models to improve asset visibility and optimize maintenance scheduling. The region also benefits from the presence of major technology providers and industrial automation companies that support large-scale deployment of intelligent maintenance systems across manufacturing, energy, and transportation sectors.

Asia Pacific represents the fastest-growing region in the predictive maintenance market, driven by rapid industrialization, expanding manufacturing capacity, and accelerating digital transformation initiatives across emerging economies. Industries across China, India, Japan, and Southeast Asia are adopting connected asset monitoring solutions to improve operational efficiency and reduce maintenance-related downtime. Organizations are increasingly implementing IoT-based monitoring platforms, advanced analytics tools, and AI-driven diagnostics to manage large industrial equipment fleets. Growing investments in smart factories, digital infrastructure, and industrial automation are strengthening demand for predictive maintenance platforms that enable data-driven asset management and improved production reliability across diverse industrial environments.

Breakdown of Primaries

In-depth interviews were conducted with chief executive officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the predictive maintenance market.

• By Company: Tier I – 34%, Tier II – 43%, and Tier III – 23%
• By Designation: C Level – 50%, Director Level – 30%, and others – 20%
• By Region: North America – 25%, Europe – 30%, Asia Pacific – 30%, Middle East & Africa – 10%, and Latin America – 5%

The report includes the study and in-depth company profiles of key players offering predictive maintenance software and services. The major players in the predictive maintenance market include Siemens (Germany), ABB (Switzerland), Schneider Electric (France), Emerson Electric (US), Rockwell Automation (US), Honeywell (US), Hitachi (Japan), SKF (Sweden), Fluke Corporation (US), IBM (US), SAP (Germany), Oracle (US), Infor (US), IFS (Sweden), SAS Institute (US), Microsoft (US), AWS (US), Google (US), PTC (US), C3.AI (US), Augury (US), Uptake (US), Upkeep (US), Limble CMMS (US), Maintainx (US), Tractian (US), Samsara (US), Bentley Systems (US), and Hexagon AB (Sweden).

Research Coverage

This research report covers the predictive maintenance market and is segmented by offering, technology, monitoring technique, deployment mode, asset type, end user, and region. The offering segment is split into monitoring infrastructure, software, and services. The monitoring infrastructure sub-segment is bifurcated into sensors & sensing devices, imaging & inspection devices, edge monitoring infrastructure, and connectivity hardware. The software sub-segment includes asset performance management platforms, industrial IoT platforms, AI-driven predictive maintenance platforms, digital twin platforms, maintenance management applications, and visualization & analytics software. The services sub-segment is further split into consulting services, predictive maintenance strategy consulting, system integration services, data science & AI services, and managed predictive maintenance services. The technology segment is split into industrial Internet of Things (IoT), artificial intelligence and machine learning, digital twin technology, edge computing & edge AI, industrial data platforms, and computer vision for equipment inspection. The industrial data platforms sub-segment is categorized into industrial data lakes and industrial data fabric platforms. The monitoring technique segment is split into industrial vibration monitoring, thermal monitoring (infrared thermography), acoustic & ultrasonic monitoring, oil & lubrication analysis, electrical signature analysis, visual inspection, and multimodal sensor fusion monitoring. The visual inspection sub-segment is categorized into computer vision inspection and drone-based inspection. The deployment mode segment is split into cloud, on-premises, edge, and hybrid deployments. The cloud deployment sub-segment is categorized into public cloud and private cloud. The asset type segment is split into rotating equipment, electrical equipment, HVAC systems, industrial robots & automation equipment, fleet & transportation assets, and power generation equipment. The rotating equipment sub-segment is categorized into pumps, compressors, motors & turbines. The electrical equipment sub-segment is categorized into transformers, switchgear, and power distribution systems. The fleet & transportation assets sub-segment is categorized into rail assets, aviation assets, and commercial vehicle fleets. The power generation equipment sub-segment is categorized into wind turbines, gas turbines, and steam turbines. The end-user segment is divided into manufacturing, discrete manufacturing, process manufacturing, energy & utilities, oil & gas, transportation & logistics, mining & machinery, telecommunications, healthcare, smart infrastructure & buildings, data centers infrastructure, and other end users. The discrete manufacturing sub-segment is categorized into automotive, electronics & semiconductor, and industrial machinery. The process manufacturing sub-segment is categorized into chemicals, pharmaceuticals, and food & beverages. The energy & utilities sub-segment is categorized into power generation, transmission & distribution, and renewable energy. The oil & gas sub-segment is categorized into upstream, midstream, and downstream. The transportation & logistics sub-segment is categorized into railways, aviation, and ports & shipping. The healthcare sub-segment is categorized into medical equipment maintenance and hospital infrastructure. The smart infrastructure & buildings sub-segment is categorized into commercial buildings and smart city infrastructure. The regional segment comprises North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America.

The report's scope encompasses detailed information on the major factors, including drivers, restraints, challenges, and opportunities, that influence the growth of the Predictive maintenance market. A detailed analysis of key industry players has been conducted to provide insights into their business overview, solutions and services, key strategies, contracts, partnerships, agreements, product & service launches, mergers and acquisitions, and recent developments in the predictive maintenance market. This report provides a competitive analysis of emerging startups in the predictive maintenance market ecosystem.

Key Benefits of Buying the Report

The report will provide market leaders and new entrants with information on the closest approximations of the revenue numbers for the overall predictive maintenance market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to better their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market pulse and provides information on key drivers, restraints, challenges, and opportunities.

The report provides insights into the following pointers:

Analysis of key drivers (Increasing need to reduce equipment downtime and maintenance costs, Increasing adoption of IoT-enabled equipment monitoring in industrial operations), challenges (Integration of predictive maintenance solutions with legacy industrial systems, Ensuring data accuracy and reliability for predictive maintenance models), opportunities (Growing adoption of edge computing for faster equipment data processing, Growing use of AI and machine learning for predictive maintenance analytics), and restraints (High implementation and infrastructure setup costs, Data management and integration challenges across multiple equipment systems).

Product Development/Innovation: Detailed insights into upcoming technologies, research & development activities, and product & service launches in the predictive maintenance market

Market Development: Comprehensive information about lucrative markets, analysis of the predictive maintenance market across varied regions

Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the predictive maintenance market

Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of Siemens (Germany), ABB (Switzerland), Schneider Electric (France), Emerson Electric (US), Rockwell Automation (US), Honeywell (US), Hitachi (Japan), SKF (Sweden), Fluke Corporation (US), IBM (US), SAP (Germany), Oracle (US), Infor (US), IFS (Sweden), SAS Institute (US), Microsoft (US), AWS (US), Google (US), PTC (US), C3.AI (US), Augury (US), Uptake (US), Upkeep (US), Limble CMMS (US), Maintainx (US), Tractian (US), Samsara (US), Bentley Systems (US), Hexagon AB (Sweden) among others, in the predictive maintenance market. The report also helps stakeholders understand the pulse of the predictive maintenance market, providing insights into key drivers, restraints, challenges, and opportunities Show more