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Global Predictive Maintenance (PdM) Report: by Component (Hardware, Solution, Services (System Integration Services, Support and Maintenance Services, Consulting Services), Testing Type (Vibration Monitoring, Electrical Insulation, Infrared Thermography, Temperature Monitoring, Ultrasonic Leak Detector, Oil Analysis), Deployment Mode (Cloud, On-premise), Technique (Traditional Technique, Advanced Technique (IoT/Big Data Technique, Machine Learning Technique), by Vertical (Manufacturing, Energy & Utilities, Healthcare, Automotive, Aerospace and Defense, Transportation) and by Region (North America (US, Canada, Mexico), Europe (Germany, UK, France, Italy, Spain, Rest of Europe), Asia-Pacific (China, Japan, South Korea, Rest of Asia-Pacific), Middle East & Africa and South America) - Forecast till 2024
Generally, most of the nation’s implement analytical upkeep for situation-checking to gauge an asset’s performance in real time. Nevertheless, advanced methods are significantly used in developed economies such as the US, few states in Western European, and few established economies of Asia-Pacific and the Middle East region. The key element of an advanced process is the Internet of Things (IoT) technology that allows different assets and systems to connect, work cooperatively, share, analyze, and act on the data. IoT relies on predictive protection sensors to capture information, analyze it, and identify any areas that need immediate attention. In October 2018, Hitachi, Ltd launched an AI-Assisted predictive maintenance service for petrochemical plants to detect real-time operational conditions. This helps petrochemical plants to increase their operational efficiency and maintenance tasks. Predictive maintenance (PdM) is a maintenance strategy driven by predictive analytics technology. The solutions are installed to monitor and detect failures or anomalies in equipment but are engaged only upon the possibility of critical failure. This helps in deploying limited resources, increasing device or apparatus uptime, enhancing quality and supply chain processes, and improving the overall satisfaction for all the stakeholders involved. Equipment is monitored using traditional and advanced techniques which allow maintenance of the apparatus to be planned before a failure occurs. Both these techniques are outfitted with various testing or monitoring tools for vibration monitoring, electrical protection, ultraviolet thermography, temperature monitoring, ultrasonic leak detection, and oil evaluation.
The predictive maintenance (PdM) market is segmented based element, testing type, deployment, technique, vertical, and region respectively. By testing type, the PdM market has been segmented into juddering observing, electrical protection, infrared thermography, temperature monitoring, ultrasonic leak detector, oil analysis. The vibration monitoring segment accounted for the leading market share in 2018, whereas the oil analysis is expected to register the highest CAGR. By technique, the market has been classified as traditional and pioneering techniques. The advanced techniques sector has been further bifurcated into the IoT/Big Data technique, and machine learning-based technique. The traditional techniques segment settled for the larger market share in 2018, whereas the advanced techniques segment is expected to register the higher CAGR. By technique, the market has been grouped as traditional and advanced methods. The enhanced techniques segment has been further branched into the IoT/Big Data technique, and machine learning-based technique. The traditional techniques segment accounted for the larger market share in 2018, although the advanced techniques segment is expected to register the higher CAGR. By testing type, the PdM market has been segmented into vibration monitoring, electrical insulation, infrared thermography, temperature monitoring, ultrasonic leak detector, oil analysis. The vibration monitoring segment accounted for the largest market share in 2018, whereas the oil analysis is expected to reach the maximum CAGR. In March 2019, IBM launched a new collection of IIOT (industrial internet of things) solutions for predictive maintenance that uses advanced analytics and artificial intelligence technologies. The solution will minimize the risk of failure associated with physical assets including manufacturing robots, vehicles, turbines, electrical transformers, elevators, and mining apparatus. The increasing adoption of real-time streaming analytics technology is one of the driving factors for the growth of the predictive maintenance market. It requires critical computations of real-time data streamed from applications, sensors, devices, and others. It provides quick and suitable time-sensitive information and language incorporation for specialized applications. Streaming analytics is one of the pillars of predictive maintenance as it provides real-time data to automatic supervising systems to maintain asset health or to personnel to perform maintenance operations when required. By implementation, the PdM market has been segmented into cloud and on-premise. The on-premise segment settled for the larger market share in 2018, whereas the cloud segment is expected to register the higher CAGR. By vertical, the market has been segmented into manufacturing, healthcare, energy & utility, automotive, aerospace & defense, transportation, and others. The manufacturing division settled for the greatest market share in 2018, whereas the energy & efficiency segment is likely to list the highest CAGR.
Geographically the predictive maintenance (PdM) market is split in regions like North & South America, Europe, Asia-Pacific, Middle east and Africa and Rest of the world. Europe held the second-largest share in the preventive maintenance market in 2018. Europe has been divided into the UK, Germany, France, Italy, Spain, and the rest of Europe. Corresponding to the analysis, Germany is supposed to gain the greatest market share followed by the UK, France, and Italy. Some of the factors responsible for the market expansion include growth of IoT connectivity, increasing investment in predictive maintenance, and growth in the automotive sector during the forecast period. The presence of companies such as Robert Bosch GmbH, Schneider Electric SA, and SAP SE is pushing development of proactive maintenance emulsions in the region. North America settled for the major market in the predictive preservation market. Advances in technology across industries, expansion of IoT connectivity, and fast adoption of advanced technologies, particularly machine learning, are some of the factors responsible for the growth of the predictive maintenance market in the region. The leading players in the region involve IBM Corporation, Oracle Corporation, Microsoft Corporation, XMPro, and RapidMiner that manage throughout the region.
The proposed spectators in the predictive maintenance (PdM) market are manufacturers, Retailers, distributors, wholesalers, Investors and trade experts, Governments, associations, industrial bodies, etc. The major companies functioning in the predictive maintenance (PdM) market are concentrating on firming their global ways by entering into untouched markets. The projected onlookers in the predictive maintenance (PdM) market are companies like IBM Corporation (US), Axiomtek Co. Ltd (Taiwan), Oracle Corporation (US), Microsoft Corporation (US), XMPro (US), Hitachi, Ltd (Japan), SAP SE (Germany), Comtrade (Ireland), C3 IoT (US), Software AG (Germany) and RapidMiner (US).
Market analysis
Generally, most of the nation’s implement analytical upkeep for situation-checking to gauge an asset’s performance in real time. Nevertheless, advanced methods are significantly used in developed economies such as the US, few states in Western European, and few established economies of Asia-Pacific and the Middle East region. The key element of an advanced process is the Internet of Things (IoT) technology that allows different assets and systems to connect, work cooperatively, share, analyze, and act on the data. IoT relies on predictive protection sensors to capture information, analyze it, and identify any areas that need immediate attention. In October 2018, Hitachi, Ltd launched an AI-Assisted predictive maintenance service for petrochemical plants to detect real-time operational conditions. This helps petrochemical plants to increase their operational efficiency and maintenance tasks. Predictive maintenance (PdM) is a maintenance strategy driven by predictive analytics technology. The solutions are installed to monitor and detect failures or anomalies in equipment but are engaged only upon the possibility of critical failure. This helps in deploying limited resources, increasing device or apparatus uptime, enhancing quality and supply chain processes, and improving the overall satisfaction for all the stakeholders involved. Equipment is monitored using traditional and advanced techniques which allow maintenance of the apparatus to be planned before a failure occurs. Both these techniques are outfitted with various testing or monitoring tools for vibration monitoring, electrical protection, ultraviolet thermography, temperature monitoring, ultrasonic leak detection, and oil evaluation.
Market segmentation
The predictive maintenance (PdM) market is segmented based element, testing type, deployment, technique, vertical, and region respectively. By testing type, the PdM market has been segmented into juddering observing, electrical protection, infrared thermography, temperature monitoring, ultrasonic leak detector, oil analysis. The vibration monitoring segment accounted for the leading market share in 2018, whereas the oil analysis is expected to register the highest CAGR. By technique, the market has been classified as traditional and pioneering techniques. The advanced techniques sector has been further bifurcated into the IoT/Big Data technique, and machine learning-based technique. The traditional techniques segment settled for the larger market share in 2018, whereas the advanced techniques segment is expected to register the higher CAGR. By technique, the market has been grouped as traditional and advanced methods. The enhanced techniques segment has been further branched into the IoT/Big Data technique, and machine learning-based technique. The traditional techniques segment accounted for the larger market share in 2018, although the advanced techniques segment is expected to register the higher CAGR. By testing type, the PdM market has been segmented into vibration monitoring, electrical insulation, infrared thermography, temperature monitoring, ultrasonic leak detector, oil analysis. The vibration monitoring segment accounted for the largest market share in 2018, whereas the oil analysis is expected to reach the maximum CAGR. In March 2019, IBM launched a new collection of IIOT (industrial internet of things) solutions for predictive maintenance that uses advanced analytics and artificial intelligence technologies. The solution will minimize the risk of failure associated with physical assets including manufacturing robots, vehicles, turbines, electrical transformers, elevators, and mining apparatus. The increasing adoption of real-time streaming analytics technology is one of the driving factors for the growth of the predictive maintenance market. It requires critical computations of real-time data streamed from applications, sensors, devices, and others. It provides quick and suitable time-sensitive information and language incorporation for specialized applications. Streaming analytics is one of the pillars of predictive maintenance as it provides real-time data to automatic supervising systems to maintain asset health or to personnel to perform maintenance operations when required. By implementation, the PdM market has been segmented into cloud and on-premise. The on-premise segment settled for the larger market share in 2018, whereas the cloud segment is expected to register the higher CAGR. By vertical, the market has been segmented into manufacturing, healthcare, energy & utility, automotive, aerospace & defense, transportation, and others. The manufacturing division settled for the greatest market share in 2018, whereas the energy & efficiency segment is likely to list the highest CAGR.
Regional analysis
Geographically the predictive maintenance (PdM) market is split in regions like North & South America, Europe, Asia-Pacific, Middle east and Africa and Rest of the world. Europe held the second-largest share in the preventive maintenance market in 2018. Europe has been divided into the UK, Germany, France, Italy, Spain, and the rest of Europe. Corresponding to the analysis, Germany is supposed to gain the greatest market share followed by the UK, France, and Italy. Some of the factors responsible for the market expansion include growth of IoT connectivity, increasing investment in predictive maintenance, and growth in the automotive sector during the forecast period. The presence of companies such as Robert Bosch GmbH, Schneider Electric SA, and SAP SE is pushing development of proactive maintenance emulsions in the region. North America settled for the major market in the predictive preservation market. Advances in technology across industries, expansion of IoT connectivity, and fast adoption of advanced technologies, particularly machine learning, are some of the factors responsible for the growth of the predictive maintenance market in the region. The leading players in the region involve IBM Corporation, Oracle Corporation, Microsoft Corporation, XMPro, and RapidMiner that manage throughout the region.
Major players
The proposed spectators in the predictive maintenance (PdM) market are manufacturers, Retailers, distributors, wholesalers, Investors and trade experts, Governments, associations, industrial bodies, etc. The major companies functioning in the predictive maintenance (PdM) market are concentrating on firming their global ways by entering into untouched markets. The projected onlookers in the predictive maintenance (PdM) market are companies like IBM Corporation (US), Axiomtek Co. Ltd (Taiwan), Oracle Corporation (US), Microsoft Corporation (US), XMPro (US), Hitachi, Ltd (Japan), SAP SE (Germany), Comtrade (Ireland), C3 IoT (US), Software AG (Germany) and RapidMiner (US).
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