The predictive maintenance market is expected to grow from USD 10.6 billion in 2024 to USD 47.8 billion in 2029, at a CAGR of 35.1% during the forecast period. The predictive maintenance market is propelled by several factors, including the rising adoption of emerging technologies for gaining valuable insights, the introduction of machine learning and artificial intelligence, and the increasing emphasis on minimizing maintenance costs, equipment failures, and downtime.
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By component, the services segment to account for higher CAGR during the forecast period.
The services segment plays a crucial role in the predictive maintenance market, serving as a core component essential for the efficient operation of software solutions. Many companies are turning to intelligent devices, robust AI systems, and Industrial Internet of Things (IIoT) solutions to monitor the health and productivity of critical equipment, aiming to minimize costly production shutdowns. Remote monitoring of machinery and equipment has become a significant priority for organizations grappling with challenges in detecting machinery failures. The adoption of predictive maintenance services, including IoT, has become imperative to mitigate the risks and failures of machines across various industries. Within the services segment, managed and professional services are considered vital for enhancing overall process efficiency.
By Technique, Vibration Analysis is expected to hold the largest market size for the year 2024.
Vibration analysis is a crucial technique employed primarily for high-speed rotating equipment in predictive maintenance strategies. It enables technicians to monitor the vibrations of machines using handheld analyzers or real-time sensors integrated into the equipment itself. Machines operating optimally exhibit specific vibration patterns, which can be compared against known standards. However, as components like bearings and shafts wear down or develop faults, they generate distinct vibration patterns, signaling potential issues. By continuously monitoring equipment vibrations, trained technicians can identify deviations from normal patterns and diagnose problems early on. The range of issues detectable through vibration analysis is extensive and includes misalignment, bent shafts, unbalanced components, loose mechanical parts, and motor irregularities.
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Unique Features in the Predictive Maintenance Market
Proactive maintenance measures are made possible by using advanced analytics and machine learning algorithms to foresee equipment breakdowns and maintenance needs based on real-time data.
Integration of IoT sensors to gather and evaluate real-time data on the functioning of equipment, enabling the early identification of anomalies and possible problems.
Predictive models are being developed to optimise asset lifespan and estimate maintenance schedules by taking into account a variety of parameters, including environmental conditions, equipment usage patterns, and past maintenance data.
Deployment of remote monitoring systems that cut expenses and downtime by allowing maintenance workers to remotely diagnose problems, troubleshoot, and monitor the health of equipment without having to physically be there.
smooth interaction with current maintenance management systems to automate processes, set priorities for maintenance, and improve the efficiency of the maintenance procedure as a whole.
Major Highlights of the Predictive Maintenance Market
Offerings for predictive maintenance as a service give businesses access to predictive analytics capabilities without requiring large upfront investments in infrastructure or knowledge, making adoption and deployment simpler.
By utilising cloud infrastructure and services, cloud-based predictive maintenance solutions enable enterprises to build and operate predictive maintenance systems more successfully. They also offer scalability, flexibility, and accessibility.
Organisations may predict equipment failures, detect maintenance patterns, and optimise maintenance programmes by analysing vast volumes of data from different sources using advanced predictive analytics capabilities.
The market for predictive maintenance provides sector-specific solutions that are suited to the particular demands and specifications of a number of industries, including manufacturing, utilities, energy, transportation, and healthcare.
Over time, predictive maintenance improves asset reliability and lowers maintenance costs by switching from reactive, fix-on-failure maintenance tactics to proactive, preventive maintenance procedures.
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Top Companies in Predictive Maintenance Market
Key players operating in the predictive maintenance market across the globe are IBM (US), ABB (Switzerland), Schneider Electric (France), AWS (US), Google (US), Microsoft (US), Hitachi (Japan), SAP (Germany), SAS Institute (US), Software AG (Germany), TIBCO Software (US), Altair (US), Oracle (US), Splunk (US), C3.ai (US), Emerson (US), GE (US), Honeywell (US), Siemens (Germany), PTC (US), Dingo (Australia), Uptake (US), Samotics (Netherlands), WaveScan (Singapore), Quadrical Ai (Canada), UpKeep (US), Limble (US), SenseGrow (US), Presage Insights (India), Falcon Labs (India). These companies employ various approaches, both organic and inorganic, including introducing new products, forming strategic partnerships and collaborations, and engaging in mergers and acquisitions, to expand their presence and offerings within the predictive maintenance market.
ABB is a pioneering technology company specializing in electrification and automation solutions, driving towards a more sustainable future. With over 130 years of experience, its workforce of approximately 105,000 employees focuses on innovating industrial transformation. ABB’s diverse portfolio includes business segments like Electrification, Industrial Automation, Motion, Robotics & Discrete Automation, Corporate and Other. These segments offer a range of products and solutions, from safer electrical systems to integrated automation systems, motors, generators, drives, robotics, and machinery automation. The company’s commitment to excellence and innovation shapes its role as a leader in power and automation technologies.
ABB offer a range of solutions designed to optimize asset performance and minimize downtime. ABB Ability Predictive Maintenance suite leverages advanced analytics, machine learning, and IoT technologies to predict equipment failures before they occur. By monitoring equipment health in real-time and analyzing historical data, ABB’s solutions enable customers to prioritize maintenance tasks, extend asset lifespans, and increase operational efficiency. ABB’s predictive maintenance offerings span various industries, including manufacturing, energy, utilities, and transportation, catering to diverse customer needs worldwide. With a focus on innovation and collaboration, ABB continues to advance its predictive maintenance capabilities to meet the evolving demands of the market.
Schneider Electric is a global provider of energy management and automation solutions. It is a pioneer in manufacturing industrial engineering equipment that specializes in electricity distribution and automation management. It operates through four business segments, namely building, infrastructure, industry, and IT. The company’s smart grid portfolio consists of products and solutions, which are reliable, energy-efficient, and sustainable. Its smart grid solutions combine electricity channels with IT infrastructure to build network structures for efficient demand and supply management. Its ADMS solutions unify various model platforms under a single solution suite that includes SCADA, Document Management System (DMS), Energy Management System (EMS), Operations Management Systems (OMS), and Data Transfer Solutions (DTS). It offers advanced metering infrastructure, provides end-to-end smart metering solutions, and supports Meter Data Management (MDM). The company’s energy management products focus on analytics solutions and making energy safe, reliable, productive, efficient, and green. Its Grid Modernization and Power Structure provide solutions that connect grids with Distributed Energy Resources (DERs) to increase infrastructure reliability. The company focuses on R&D and inorganic growth strategies to cater to the evolving enterprise needs. Its low-voltage electric distribution panel, developed using the cutting-edge IoT platform, collects large volumes of data and stores it in the cloud to optimize business performance. Moreover, the company caters to a broad customer base present across 100 countries in North America, Europe, Latin America, Asia Pacific, and Middle East & Africa. It operates through 200 plants and 90 distribution centers across the globe.
Software AG is a leading independent integration, IoT, analytics, process software, and services company in the ICT industry. The company was founded in 1969 and is headquartered in Darmstadt, Germany. It offers Business Process Management (BPM), data management, and consulting services worldwide. It provides a digital business platform and digital transformation solution for banking institutions, capital markets, government organizations, manufacturing companies, commodity trading firms, and supply chain firms. Its portfolio includes solutions in the areas of customer journey design, fraud detection, omnichannel integration, smart branch monitoring, mobile enablement, SAP, real-time promotions, and cash flow risk management. In addition to this, it provides business and IT transformation, analytics and decisions, process and integration, in-memory data, and transaction processing solutions. The company’s solutions include an integration platform built on a powerful Enterprise Service Bus (ESB) that enables organizations to virtually connect any system and application quickly. It has offices in Darmstadt, Tres Cantos, Luxembourg, and Dublin, among other locations.
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