Predictive Maintenance Market Is Projected To Grow Around USD22.429 Billion By 2026 Due To The Proliferation Of Industry 4.0 | TechSci Research

The global predictive maintenance market was valued at USD4.270 billion in 2020 and is projected to grow around USD22.429 billion by 2026 due to the proliferation of industry 4.0, wireless communication, rising artificial intelligence, machine learning, and IoT are expected to drive industry growth in the forecast period. However, growth in the industry was hampered in the year 2020, owing primarily to global lockdowns that resulted in a halt in economic activity, but the demand recovered after the second quarter.

Predictive maintenance is the application of data-driven, proactive repair approaches to analyze equipment status and anticipate when maintenance should be conducted. Predictive maintenance software employs artificial intelligence and machine learning techniques, as well as predictive analytics, to forecast when a piece of equipment will malfunction, enabling preemptive maintenance to be scheduled before the failure occurs. The goal is to conduct maintenance at the most convenient and cost-effective time possible, maximizing the equipment's lifespan while preventing it from being affected.

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Company Profiles

Schneider Electric SE, Hitachi Technologies Co Ltd, IBM Corp., Siemens AG, Bosch Software Innovations GmbH, Microsoft Corporation, TIBCO Software Inc., C3 Inc., SAP SE, Software AG, PTC Inc., General Electric Company, etc. are the major players operating in the global predictive maintenance market.

Increasing Demand in Aerospace and Defense to Boost the Growth

Predictive maintenance is increasingly becoming the most significant strategy across many industries, especially in Aerospace, due to the growing need for greater operational reliability, lower maintenance costs, and increased safety. Machine Learning-based Diagnostics and Prognostics techniques, as opposed to traditional approaches in building Predictive maintenance solutions, are becoming increasingly popular as newer aircraft are equipped with more sensors. Using predictive maintenance in aerospace and defense provides real-time diagnostics using cloud service providers such as Amazon Web Services that have the potential to detect patterns and enable early failure detection and isolation. Predictive msaintenance also provides real-time flight assistance that blocks temporary inconsistencies between airspeed measurements which occurs due to speed sensors being covered by ice crystals.

Use of Industry 4.0 to Positively Influence the Growth

Predictive maintenance involves collecting and evaluating data from machines to increase efficiency and optimize the maintenance process. Implementing the technology industry 4.0 for predictive maintenance leads to higher process transparency, lower maintenance costs, reduced machine downtime, enhanced product lifespan, etc. Industry 4.0 enables the possibility of identifying patterns of behavior that more precisely predict when the equipment is going to experience a failure. In manufacturing industries, heat exchangers can experience blockage due to clogs which can cause serious complications resulting in manufacturing errors and hours of downtime. By measuring temperature differences upstream and downstream of the heat exchanger, a threshold value can be estimated which can be used in predictive maintenance software to be used as an alert sign. Revolutionary technology industry 4.0 comprises integrated systems, predictive maintenance, additive manufacturing, augmented reality, internet of things, simulation, autonomous robots, a platform, and cyber security, which works in a loop to complete a process.

Increased Usage of Logistics and Transportation to Drive the Market Growth

By predicting when parts might fail based on performance, data and information, predictive maintenance has the potential to assist transportation industries to avoid machinery breakdowns while lowering maintenance costs. Predictive maintenance in the transportation and logistics business uses AI-assisted maintenance, allowing users to make repair decisions based on the vehicle's present state rather than pre-determined time intervals. Inspection and preservation of transport devices and equipment have always been used to maintain logistics and transportation resources. In today's world, new technologies are having an increasing impact on the transportation industry. Artificial intelligence and other data processing tools enable intelligent and speedy data interpretation, resulting in a shift in corporate planning and maintenance tasks. Predictive analytics is being used to satisfy the rising needs of industries as this tool has been acknowledged as having the greatest impact on the supply chain by the logistics and transportation industries.


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Market Segmentation

The global predictive maintenance market can be segmented based on Component, Testing type, Deployment type, Organization Size, End User, and Region. Based on Component, the market can be segmented into service and solution. In terms of Testing Type, the market is segmented into Vibration Analysis, Power System Assessment, Infrared Thermal Inspections, Insulating Fluid Analysis, Circuit Monitoring Analysis, Others. Based on Deployments, the market is segmented into On-Premise and Cloud. Based on the Organization Size, the market is segmented into SME and Large Enterprise. Based on the end user, the market is bifurcated into Aerospace and Defense, Energy and Infrastructure, Logistics and Transportation, Manufacturing, Oil and Gas, Automotive, Retail and Ecommerce, Others. The market analysis also studies the regional segmentation to devise regional market segmentation, divided among North American region, European region, Asia-Pacific region, Middle East & African region and South American region.

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