US Predictive Maintenance Market

According to a research report "US Predictive Maintenance Market Size, by Technology (Analytics, Data Management, AI, IoT Platform, Sensors), Technique (Vibration Analysis, Infrared Thermography, Oil analysis, Motor Circuit Analysis, Acoustic Monitoring) - Global Forecast to 2029", published by MarketsandMarkets, the US predictive maintenance market is expected to reach USD 15.2 billion in 2029 from USD 3.6 billion in 2024, at a CAGR of 32.8% during the forecast period.

The US predictive maintenance market is driven by the increasing adoption of IoT, AI, and machine learning in industrial operations, reducing downtime and maintenance costs. Regulatory compliance and asset optimization further fuel demand. However, challenges include high implementation costs, data security concerns, and the need for skilled professionals to manage advanced predictive analytics systems.

The solution segment accounted for the largest share by component segment in the US predictive maintenance market in 2024.

The solutions segment of the predictive maintenance market in the U.S. is projected to capture the largest share, thanks to the growing use of advanced analytics, AI-powered diagnostics, and IoT-enabled monitoring systems across various industries. Companies are increasingly turning to predictive maintenance solutions to minimize unexpected downtime, boost asset performance, and prolong the lifespan of their equipment. There's a noticeable rise in demand for both cloud-based and on-premise predictive maintenance software, especially in sectors like manufacturing, energy, and transportation, where real-time data insights significantly improve operational efficiency. Moreover, the surge in investments in Industry 4.0 technologies is further solidifying the lead of solutions over services in the US.

The vibration analysis segment accounted for the largest share by technique segment in the US predictive maintenance market in 2024.

Vibration analysis is projected to dominate the largest market share in the US predictive maintenance market because of its efficiency in identifying early indicators of mechanical failures in essential rotating machinery like motors, turbines, and compressors. Industries such as manufacturing, energy, and aerospace heavily depend on vibration monitoring to spot issues like imbalances, misalignments, and bearing faults before they escalate into expensive breakdowns. Its non-invasive nature, remarkable accuracy, and ability to provide real-time diagnostics make it a favored option for asset maintenance. Additionally, the growth of IoT and wireless sensors has made vibration analysis more accessible and widely adopted across different sectors.

The artificial intelligence (AI) segment is expected to grow at the highest rate during the forecast period.

Artificial intelligence (AI) technology is set to grow fastest in the US predictive maintenance market due to its knack for boosting data-driven decision-making, automating the detection of anomalies, and making highly accurate failure predictions. By harnessing machine learning algorithms, deep learning, and neural networks, AI-powered predictive maintenance dives into massive datasets collected from IoT sensors, providing real-time insights and proactive maintenance strategies. As more industries turn to AI solutions to cut operational costs, reduce downtime, and enhance asset performance, the demand for AI-based predictive maintenance is skyrocketing, especially in sectors like manufacturing, energy, and transportation, fueling its rapid expansion.

Key players in the US predictive maintenance market are IBM (US),  AWS (US), Google (US), Microsoft (US), SAS Institute (US), TIBCO Software (US), Altair (US), Oracle (US), Splunk (US), C3.ai (US), Emerson (US), GE (US), Honeywell (US), PTC (US), Uptake (US), UpKeep (US), Limble (US), SenseGrow (US).

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