Global Machine Condition Monitoring Market To 2030: Shift From Reactive Maintenance To

DUBLIN--(BUSINESS WIRE)--The "Global Machine Condition Monitoring Market Size, Share, Growth Analysis, By Monitoring Technique, By Offering, By Deployment, By Monitoring Process - Industry Forecast 2023-2030" report has been added to ResearchAndMarkets.com's offering.

The machine condition monitoring market size was valued at USD 2.6 billion in 2021 and is poised to grow from USD 2.8 billion in 2022 to USD 5.51 billion by 2030, growing at a CAGR of 7.8% in the forecast period (2023-2030).

The machine condition monitoring market refers to the industry that provides solutions and services for monitoring the health and performance of machinery and equipment. It involves the use of various techniques, sensors, and software to collect data, analyze machine behavior, and detect any potential faults or abnormalities.

Machine condition monitoring helps in proactive maintenance, reducing downtime, optimizing asset performance, and preventing costly equipment failures.

Segments covered in this report

The global machine condition monitoring market is segmented on the basis of monitoring technique, offering, deployment, monitoring process and region.

Based on monitoring technique, the market is segmented into vibration monitoring, thermography, oil analysis, corrosion monitoring, ultrasound emission and motor current analysis. Based on offering, the Machine Condition Monitoring market is segmented into: hardware and software.

Based on deployment, the market is segmented into on-premises and cloud. Based on monitoring process, the market is segmented into: online condition monitoring and portable condition monitoring. Based on region, the global machine condition monitoring Machine Condition Monitoring market is segmented into North America, Europe, Asia-Pacific, South America, and MEA.

Driver

The shift from reactive maintenance to proactive maintenance strategies is a major driver for the machine condition monitoring market. By continuously monitoring machine health, organizations can detect early signs of equipment failure, schedule maintenance activities in advance, and avoid unplanned downtime.

The rapid adoption of industrial automation across various sectors has increased the demand for machine condition monitoring. Automated systems heavily rely on machines and equipment, making it essential to monitor their condition continuously to ensure smooth operations and prevent breakdowns.

Restraint

Effective machine condition monitoring requires skilled personnel who can interpret the collected data and perform accurate analysis. The shortage of skilled professionals in this field can pose a challenge to the widespread adoption of machine condition monitoring.

Market Trends

The integration of the Internet of Things (IoT) and artificial intelligence (AI) technologies is a significant trend in the machine condition monitoring market. IoT enables the collection of real-time data from sensors embedded in machines, while AI techniques analyze the data to provide actionable insights and predictive maintenance recommendations.

Cloud computing has facilitated the adoption of machine condition monitoring by offering scalable and cost-effective storage and analysis of machine data. Cloud-based solutions allow organizations to access monitoring systems from anywhere, facilitate data sharing, and enable remote monitoring and diagnostics.

Companies Mentioned

Emerson Electric
Honeywell International
SKF AB
General Electric Company
Rockwell Automation, Inc.
Parker Hannifin Corporation
National Instruments Corporation
Schaeffler Technologies AG & Co. KG
Bruel & Kjaer Sound & Vibration Measurement A/S
Fluke Corporation
PCB Piezotronics, Inc.
ALS Limited
Azima DLI Corporation
Commtest Instruments Ltd.
Meggitt PLC
PRUFTECHNIK Dieter Busch AG
SPM Instrument AB
Parker Kittiwake
Testo SE & Co. KGaA
Yokogawa Electric Corporation

For more information about this report visit https://www.researchandmarkets.com/r/voh5fu

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First published on Mon, Jun 19, 2023

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