Machine Condition Monitoring Market Opportunities and Challenges detailed Report 2026

The machine condition monitoring market study offers a thorough analysis of the key factors, market figures in terms of revenues, segmental data, regional data, and country-wise data. The global machine condition monitoring market size is projected to grow from USD 2.6 billion in 2021 to USD 3.6 billion by 2026; it is expected to grow at a CAGR of 7.1% from 2021 to 2026.

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The machine condition monitoring market was moderately impacted by the COVID-19 pandemic. In the prevailing COVID-19 situation, the expected growth of the machine condition monitoring market is expected to decline as the pandemic is restricting the supply and adoption of hardware components, such as sensors, in a number of applications. This is due to the severe disruptions in businesses and the global economy. However, not all semiconductor companies are witnessing a decline in growth as remote monitoring of production activities has allowed companies to continue working during lockdowns as well. It is expected that the demand for condition monitoring systems and solutions will increase in the coming years, and the market is expected to recover in 2021. After the recovery phase, the machine condition monitoring market is projected to grow at a moderate rate until 2026

The key difference between predictive maintenance and condition monitoring is that condition monitoring focuses on monitoring real-time conditions of machines, while predictive maintenance focuses on the detection of faults before their occurrence. Predictive maintenance is gaining rapid popularity in the manufacturing sector globally owing to the benefits offered by it. It is carried out only when machinery deployed in plants requires repairs to avoid impending failures so that the necessary, cost-effective downtime can be scheduled according to convenience

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Some operators rely on their instincts and experiences to judge the condition of machines that is not always a reliable option. There may be instances wherein operators can overrule the analysis based on their personal experiences or due to the lack of trust in the prediction capabilities of condition monitoring technology. Experienced operators are at times skeptical about the diagnostics and predictions provided by machine condition monitoring systems owing to mistakes made by these machines in the form of dropped metrics, service outages, and unreliable alerting signals. Moreover, operators may not accept the new way of planning and maintaining the performance of machines, thereby resulting in disapprovals for the implementation of successful machine condition monitoring programs. This is due to the complexity of production systems that may include multiple assets on which it may be difficult to install machine condition monitoring systems and solutions.

The advent of machine learning, coupled with efficient big data analytics and a parallel processing framework, is transforming the machine condition monitoring market. Big data analytics has accelerated the process of the analysis of the data generated by condition monitoring systems owing to its capability of processing large volumes of data. Machine learning carries out easy benchmarking of machine performance, enables transparency in data, and supports efficient collaboration among different processes of condition monitoring systems. Big data-enabled condition monitoring helps companies in lowering the overall cost of ownership of assets and enables them to run their business efficiently.

There has been a growing requirement of integrating machine condition monitoring systems with other machine maintenance systems such as plant asset management (PAM) systems and computerized maintenance management systems (CMMS). The integration of vibration monitoring systems with PAM and CMMS helps in ensuring that the results from different analyses offer a holistic view of the condition of assets and avoid costly downtime. This development presents a key challenge to the growth of the machine condition monitoring market. Though the initiative to integrate machine condition monitoring techniques with management systems, such as PAM and CMMS, is expected to be beneficial for asset management to a large extent, the integration of condition monitoring systems with other management systems is expected to hamper the demand for machine condition systems as individual systems.

The machine condition monitoring market players have implemented various types of organic as well as inorganic growth strategies, such as product launches, product developments, and acquisitions to strengthen their offerings in the market. Emerson Electric Co. (US), General Electric Co. (US), Honeywell International Inc. (US), National Instruments Corp. (US), SKF AB (Sweden), ALS Ltd. (Australia), Wilcoxon Sensing Technologies Inc. (US), Parker Hannifin Corp. (US), Rockwell Automation Inc. (US), Schaeffler AG (Germany), Bruel & Kjaer (Denmark), Symphony Industrial AI Inc. (US), are some of the major players in machine condition monitoring market are some of the major players in machine condition monitoring market.

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