The global machine condition monitoring market size is expected to grow from USD 3.1 billion in 2024 to USD 4.7 billion by 2029, at a CAGR of 8.3% from 2024 to 2029. Condition monitoring helps analyze a range of parameters to determine the probability of machinery breakdowns. Parameters that can be analyzed are temperature, vibration, corrosion, torque, alignment, and oil and wear debris. Thus, the information on the health of machines helps detect machine faults in advance to prevent costly repairs and significant damages to machines or even a sudden complete failure of a machine.

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By monitoring technique, thermography is expected to grow at an impressive CAGR during the forecast period. Thermo signifies heat or increased temperature. Temperature is one of the key indicators of the structural health of machines and components. Abnormal temperature distribution can be caused by various conditions, such as faulty machinery, corroded electrical connections, and damaged material components. This machine condition monitoring technique has several applications, including detecting misalignment, improper lubrication, imbalances, stress, and wear in mechanical parts. It also identifies overheating, pipe leaks, and pressure vessel weaknesses in electrical equipment.

By offering, hardware segment is expected to grow at an impressive CAGR. Hardware components are the most important elements of machine condition monitoring systems. The integration of hardware components enables the formation of the complete machine condition monitoring system. This whole system can perform the task of machine health monitoring based on various parameters such as vibration, temperature, and corrosion, which can be measured and supervised. Different hardware components, including vibration sensors, infrared sensors, spectrometers, ultrasound detectors, spectrum analyzers, and corrosion probes, are used in machine condition monitoring systems.

The oil & gas industry segment is expected to be one of the major adopters of machine condition monitoring systems. In oil refineries, machine condition monitoring systems are deployed to monitor pipes and tanks installed in plants. They are extremely useful for monitoring the pressure and flow in pipelines and detecting any impending blockages or accidents. IIoT-based machine condition monitoring systems are increasingly used by oil and gas companies as these systems allow remote monitoring of multiple devices from any location. It makes monitoring pipelines, offshore drilling rigs, and sea-based wind turbine installations much easier. These IIoT-connected monitoring systems can automatically collect, aggregate, and disperse real-time data to technicians anywhere in the world. The falling prices of sensors and their easy-to-use designs are the major drivers for the increased adoption of machine condition monitoring systems in the oil & gas industry. Corrosion monitoring also plays a critical part in assessing the health of assets of refineries that are constantly subject to harsh environments with high pressure and temperature conditions.

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The machine condition monitoring companies in Europe has been studied for the UK, Germany, France, and Rest of Europe. Maintenance costs account for a significant share of operational costs in any asset-intensive industry. For large companies, reducing maintenance expenditure can significantly help increase their profits. Verticals such as manufacturing, energy and utilities, and transportation and logistics in Europe use machine condition monitoring systems and solutions to improve their asset performance. Germany, the UK, and France are the top three markets in Europe, witnessing increased demand for machine condition monitoring systems from manufacturing firms. Moreover, low costs of sensors and seamless internet connectivity are fueling the incorporation of predictive analytics and big data technologies in the industrial sector. Industries in Europe adopt machine condition monitoring systems primarily to ensure constant visibility into assets and their performance and prevent financial losses by avoiding downtime.