Machine Condition Monitoring Market Analysis & Forecast: 2025-2032

Machine Condition Monitoring Market Size and Forecast – 2025 to 2032

The machine condition monitoring market is estimated to be valued at USD 3.62 Bn in 2025 and is expected to reach USD 6.54 Bn by 2032, growing at a compound annual growth rate (CAGR) of 8.8% from 2025 to 2032.

Key Takeaways

• By Offerings, Hardware holds the largest market share of 58.7% in 2025 owing to its advancements in sensor technology.
• By Deployment, On-premises acquired the prominent market share of 73.13% in 2025 owing to its data security & sovereignty.
• By End-Use Industry, Automotive expected to hold largest market share of 23.26% in 2025 owing to the high value of automotive manufacturing equipment.
• By Region, North America dominates the overall market with an estimated share of 39.4% in 2025 owing to the high industrial automation penetration.

Market Overview

The Machine Condition Monitoring (MCM) market is expanding rapidly as industries increasingly automate processes, adopt predictive maintenance, and integrate IoT and Industry 4.0 technologies. Companies use MCM systems to monitor machinery in real time, minimize unplanned downtime, enhance operational efficiency, and prolong equipment lifespan across sectors like automotive, energy, manufacturing, and aerospace. Businesses are investing heavily in advanced sensors, analytics, and AI-driven monitoring solutions, while adhering to strict safety and regulatory standards, which is driving global market demand.

Current Events and Its Impact on the Machine Condition Monitoring Market

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Role of AI (Artificial Intelligence) in Machine Condition Monitoring

AI plays a transformative role in machine condition monitoring by enabling continuous, data-driven insights that improve reliability and reduce unplanned downtime. By analysing vibration, temperature, pressure, and performance patterns in real time, AI can detect subtle anomalies long before they become failures. Machine-learning models learn normal operating behaviour and identify deviations with high accuracy, providing early warnings and actionable diagnostics. This allows maintenance teams to shift from reactive or routine schedules to predictive strategies, optimising resources and extending equipment lifespan. Overall, AI enhances uptime, safety, and efficiency while minimising costs across industrial operations.

Alfa Laval’s Clariot™, launched in June 2025, is an AI-driven condition-monitoring system for hygienic process equipment. It delivers precise, real-time analysis to boost uptime and optimise resource use. Unplanned stoppages in hygienic industries cost billions each year through product loss, extra cleaning, delays, wasted water, and equipment damage. Clariot™ tackles this by providing 24/7 monitoring, alerts, and diagnostics for pumps and other rotating machinery, enabling early fault detection, preventing failures, and helping plants operate at full capacity with fewer interruptions and longer equipment life.

Segmental Insights

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Machine Condition Monitoring Market Insights, By Offering - Hardware contributes the highest share of the market owing to its ruggedization for harsh industrial environments

Hardware holds the largest market share of 58.7% in 2025. Industries are driving the demand for hardware in the Machine Condition Monitoring (MCM) market to collect reliable, real-time data from machinery. They deploy sensors, vibration analyzers, and data acquisition devices to detect faults early, prevent equipment failures, and ensure continuous operations. The increasing adoption of IoT-enabled devices and edge computing is accelerating the use of advanced monitoring hardware. Companies focus on durable, high-precision equipment to deliver accurate measurements, integrate smoothly with existing systems, and boost operational efficiency across multiple sectors.

Machine Condition Monitoring Market Insights, By Deployment - On-premises contribute the highest share of the market owing to its customization and integration

On-premises acquired the prominent market share of 73.13% in 2025. Organizations are driving the growth of on-premises solutions in the Machine Condition Monitoring (MCM) market to gain greater control over their data and infrastructure. They use on-premises systems to securely store and process sensitive operational information internally, meeting strict security and regulatory requirements. Companies apply these solutions for real-time monitoring, fault detection, and maintenance planning without depending on external networks. On-premises deployment also allows seamless integration with legacy machinery, minimizes latency, and delivers customized analytics to enhance equipment performance. For instance, in November 2025, Festo launched Festo AX Motion Insights Electric, an AI-based tool that predicts wear and detects anomalies in electric axes and servo drives to reduce unplanned downtime. It also supports on-premises computing, giving users full control over their data.

Machine Condition Monitoring Market Insights, By End-Use Industry - Automotive contribute the highest share of the market owing to its growth of electric vehicles (EVS) & powertrain monitoring

Automotive expected to hold largest market share of 23.26% in 2025. The automotive sector is driving the Machine Condition Monitoring (MCM) market demand to ensure complex manufacturing equipment and vehicle components operate reliably and efficiently. Manufacturers monitor assembly lines, robotics, and powertrain systems with MCM tools, detecting potential faults before they cause costly downtime. The expansion of electric and hybrid vehicles increases the need to track batteries, motors, and electronic systems. Automotive companies also implement advanced sensors and analytics to optimize maintenance, boost production quality, and extend the lifespan of critical machinery. For instance, in September 2025, CarMD today launched CarMD® Connect, an all-in-one smart solution for vehicle health monitoring and location sharing.

Regional Insights

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North America Machine Condition Monitoring Market Trends

North America dominates the overall market with an estimated share of 39.4% in 2025. North America’s industrial base and widespread adoption of IIoT and smart-manufacturing solutions are driving the Machine Condition Monitoring market. Companies use real-time analytics and AI-powered predictive maintenance to reduce downtime across aerospace, energy, and automotive sectors. They increasingly deploy wireless sensor networks and edge computing to detect faults quickly and respond efficiently. Additionally, firms actively monitor and optimize critical equipment to meet strict regulatory requirements and high safety standards, ensuring reliable and efficient operations throughout their facilities. For instance, in October 2025, SPM Instrument North America has launched its new website, www.spmnorthamerica.com, under CEO Bill Partipilo, aiming to expand its presence in the U.S. and Canada and strengthen its leadership in condition monitoring technology.

Asia Pacific Machine Condition Monitoring Market Trends

Rapid industrialization in countries like China, India, and Japan is driving growth in the Asia Pacific Machine Condition Monitoring market. Companies are actively investing in predictive maintenance and using vibration and thermography techniques to ensure asset reliability. Governments promote automation through initiatives such as “Made in China 2025” and policies supporting manufacturing expansion. Meanwhile, firms are leveraging increasing IIoT adoption and real-time analytics to monitor equipment more intelligently, minimize downtime, and enhance overall productivity across industrial operations. For instance, in November 2025, the Indian Register of Shipping and Neptunus Power Plant signed an MoU to launch the world’s first indigenous marine engine condition-monitoring technology, showcasing India’s ability to develop globally compliant, export-ready maritime solutions.

United States Machine Condition Monitoring Market Trends

Manufacturers in the U.S. are driving the evolution of the Machine Condition Monitoring market by adopting Industry 4.0 and smart maintenance practices. Companies are increasingly using IIoT-enabled sensors and real-time analytics to identify equipment faults before they cause costly downtime. Leading players in energy, aerospace, and automotive actively apply predictive maintenance to maximize asset performance. Additionally, firms are investing in edge computing and wireless monitoring to expand diagnostic capabilities while maintaining secure, localized data processing. For instance, John Deere recently opened its new Machine Health Monitoring Center at the Dubuque Works facility in Iowa to monitor machines and respond faster to critical issues that cause costly downtime.

India Machine Condition Monitoring Market Trends

India’s manufacturing and power sectors are driving growth in the Machine Condition Monitoring market through ongoing modernization. Domestic companies are deploying vibration analysis, thermal imaging, and oil monitoring to prevent unplanned failures and improve efficiency. The “Make in India” initiative, along with increasing investment from global automotive players, is boosting demand for predictive maintenance. At the same time, firms are adopting IIoT, real-time analytics, and machine-learning technologies to make condition monitoring more effective and widely accessible across India’s industrial landscape. For instance, in August 2025, BITS Pilani Hyderabad has launched smart health monitoring systems for MSME machines, developing two innovative solutions to help Indian SMEs improve their manufacturing processes.

Machine Condition Monitoring Market Trend

Deepening Shift to Predictive Maintenance

Industries are intensifying their move from reactive or scheduled upkeep to truly predictive maintenance. By continuously monitoring vibration, thermal, acoustic, and electrical signals, condition‑monitoring systems can flag early signs of failure. This enables maintenance teams to intervene precisely when needed, reducing unplanned downtime, extending equipment life, and saving money. Organizations are now treating machine health as a strategic asset—and leveraging this insight to optimize resource allocation and achieve higher operational resilience.

Rise of IoT‑Enabled & Wireless Sensor Networks

The proliferation of IoT is revolutionizing how machines are monitored. Smart, wireless sensor networks (measuring vibration, temperature, ultrasound, etc.) make it far easier to install and scale condition‑monitoring systems, eliminating much of the cabling and infrastructure constraints of older setups. These sensors transmit data in real time, enabling constant asset visibility—even in remote or difficult-to-access areas—thereby paving the way for more agile, responsive maintenance strategies.

Machine Condition Monitoring Market Opportunity

Expansion into Green Energy Sector

With increasing investments in renewable energy, condition‑monitoring systems can be tailored for wind turbines, solar inverters, and battery storage. These high‑value assets require continuous health tracking to avoid downtime and optimize performance. By offering specialized sensor systems and predictive maintenance tools for renewables, providers can tap into a fast‑growing asset class, providing value in maximizing uptime, forecasting failures, and minimizing operational risk in sustainable energy infrastructure.

Market Report Scope

Machine Condition Monitoring Market News

• In March 2025, Salunda has launched the Latch Hawk 2 fingerboard latch monitoring device to prevent dropped objects and enhance safety during pipe handling on offshore drilling rigs.
• In November 2025, Shell Marine has launched Shell Marine Sensor Service (SMSS), an onboard monitoring service that gives real-time insights into oil and equipment condition to help shipping operators boost vessel uptime and operational resilience.

Analyst Opinion (Expert Opinion)

• Condition monitoring is becoming a mission-critical operational lever. The financial stakes are just too high: unplanned downtime can cost hundreds of thousands per hour in heavy industries. Firms that lean into real-time, AI-enabled monitoring are dramatically shifting the risk equation. For example, Siemens’ Senseye solution reportedly halved unplanned downtime, boosted maintenance staff productivity by over 50%, and slashed maintenance costs by about 40%, recouping its investment in just a few months.
• The most compelling narrative isn’t just “monitoring for failure detection” — it’s predictive prescience. One real-world case: a plastic-film manufacturer detected gearbox radial play early via wireless vibration sensors and avoided a catastrophic failure, averting roughly 1,200 hours of downtime and saving more than US$ 1.2 million in a year. That kind of return fundamentally alters how maintenance teams behave – from firefighting to strategic planning.
• Moreover, AI sophistication is no longer academic. Emerging models — like the transformer-based neural nets in recent research — achieved nearly 71% accuracy in predicting breakdowns one hour ahead, improving production yield from ~78% to ~90%. That’s not just being reactive; it’s being anticipatory.
• Companies that master MCM not just for detection but for insight-driven decision-making will outperform peers in reliability and cost control. But vendors and industrial leaders need to treat it as a core strategic function, not a side digital project. Those who do will unlock outsized ROI and transform maintenance from a cost center into a competitive moat.

Market Segmentation

• Offerings Insights (Revenue, USD Bn, 2020 - 2032)
• Hardware
• Vibration Sensors
• Infrared Sensors
• Spectrometers
• Corrosion Probes
• Ultrasound Detectors
• Spectrum Analyzers
• Software
• Services
• Deployment Insights (Revenue, USD Bn, 2020 - 2032)
• Cloud
• On-premises
• End-use Industry Insights (Revenue, USD Bn, 2020 - 2032)
• Automotive
• Oil and Gas
• Power Generation
• Chemicals
• Metals and Mining
• Aerospace and Defense
• Food and Beverages
• Others
• Regional Insights (Revenue, USD Bn, 2020 - 2032)
• North America
• U.S.
• Canada
• Latin America
• Brazil
• Argentina
• Mexico
• Rest of Latin America
• Europe
• Germany
• U.K.
• Spain
• France
• Italy
• Russia
• Rest of Europe
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• ASEAN
• Rest of Asia Pacific
• Middle East & Africa
• GCC Countries
• South Africa
• Israel
• Rest of Middle East & Africa
• Key Players Insights
• Advanced Technology Services, Inc.
• Allied Reliability,
• Analog Devices Inc.,
• Baker Huges Company
• Crystal Instruments
• Dewesoft
• Emerson Electric Co.
• Fluke Corporation
• General Electric,
• Honeywell International
• Meggit Plc,
• National Instruments Corporation
• Parker Hannifin Corporation
• Rockwell Automation Inc.
• Schaeffler AG SKF
• Amphenol Inc.

Sources

Primary Research interviews

• Maintenance engineers and reliability managers in heavy‑industry plants (e.g., rotating‑equipment, manufacturing)
• Data scientists / IIoT architects working on condition‑monitoring projects
• R&D heads at companies building sensors or edge-computing systems for condition monitoring

Databases

• MIMII Dataset — public dataset of machine sound under normal/anomalous conditions
• PubMed / NCBI — technical publications, e.g., edge‑computing based monitoring systems
• arXiv — pre‑prints of ML architectures (e.g., autoencoders, reinforcement learning) for condition‑monitoring

Magazines

• Industry‑IoT Consortium publications / newsletters (they run a Condition Monitoring & Predictive Maintenance Testbed)
• Trade‑engineering magazines (e.g. IEEE Spectrum, Control Engineering) covering IIoT and maintenance use cases (for example, researchers presenting IoT + ML‑based maintenance)

Journals

• Machines (MDPI) – special issue on Machine Learning‑based Predictive Maintenance & Condition Monitoring
• Sensors (MDPI) – special issue on Sensors for Predictive Maintenance
• Electronics (MDPI) – “Advances in Machine Condition Monitoring and Fault Diagnosis”
• Applied Sciences – “Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines”
• Sensors (Basel) – condition monitoring in railway systems using semi‑supervised ML

Newspapers

• Technical / industrial sections of major newspapers (e.g., Financial Times, The Economic Times) covering smart manufacturing, predictive maintenance, or IIoT adoption (you would cite specific relevant articles based on your research)
• Local trade‑press covering maintenance / reliability in manufacturing hubs (e.g., India, Germany, US)

Associations

• PHM Society (Prognostics and Health Management Society) — a non-profit covering prognostics and maintenance engineering
• Industry IoT Consortium — especially their Condition Monitoring & Predictive Maintenance Testbed

Public Domain sources

• Wikipedia entries (for background / definitions) — e.g., “Proactive maintenance” “Ferrography” (oil‑analysis method)
• Open‑access research on condition-monitoring systems / algorithms from institutional repositories
• Open‑source PHM datasets such as MIMII from Zenodo (via arXiv publication)
• Academic pre-prints (arXiv) for new methods / architectures in condition monitoring

Proprietary Elements:

• CMI Data Analytics Tool
• Proprietary CMI Existing Repository of information for last 8 years