The machine condition monitoring market is estimated to be valued at USD 3.62 billion in 2025 and is expected to reach USD 6.54 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.8% from 2025 to 2032.
The machine condition monitoring market is expected to witness significant growth over the forecast period.
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Advent of industrial IoT and increase in adoption of predictive maintenance is expected to drive the market. Growing need for reduction in maintenance cost and increasing focus on asset management will further aid in the market growth. Predictive maintenance with the integration of advanced technologies helps in minimization downtime. This is anticipated to propel the demand for machine condition monitoring solutions across various industries.
Improving Operational Efficiency and Preventing Unplanned Downtime
One of the key drivers for the growth of machine condition monitoring market is the need for improving operational efficiency of industrial equipment and machinery across various industry verticals. Continuous monitoring of various parameters helps in detecting even small changes in the working of equipment which could possibly indicate a future breakdown. This helps in planning maintenance activities in advance instead of facing an unexpected shut down of the equipment. Unplanned downtime of industrial machinery can disrupt production schedules and lead to loss of potential revenue. Effective condition monitoring allows manufacturers to optimize maintenance work and reduce unexpected breakdowns. This helps improve overall equipment effectiveness and maximize asset usage. The ability to plan maintenance windows helps reduce maintenance costs too. Many organizations are now able to move from reactive to predictive and preventive maintenance with machine condition monitoring systems. This has helped boost operational efficiency significantly.
For instance, in April 2022, Emerson is a technology, software, and engineering company, considered updating its machinery health platform to leverage a modern interface for identifying equipment failures and improving plant availability.
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