Condition-based maintenance<\/a> plays a vital role in enhancing digital infrastructure efficiency and resource management.<\/p>\nWhen AI is added into the mix, critical infrastructure monitoring\/condition based maintenance can be taken to new heights. AI algorithms, when fuelled by vast amounts of data collected from monitoring systems, can provide advanced analytics. This empowers systems managers to make adjustments for physical behaviours and plan for the end-of-life of various equipment components.<\/p>\n
AI not only aids in being prepared for potential equipment failures, maintenance needs, and environmental risks but also facilitates adaptive infrastructure optimisation.<\/p>\n
By learning from data collected over time, AI algorithms can help to determine the most effective ways to improve IT performance and recommend adjustments to optimise the use of critical equipment. This includes identifying opportunities to reduce energy waste, enhance efficiency, and streamline operations.<\/p>\n
The adaptive nature of AI enables these recommendations to evolve over time, aligning with the changing dynamics of the infrastructure and improving overall performance. Such insights allow operators to minimise downtime and help deliver the continuous operation of critical infrastructure, such as keeping the lights on and the internet running.<\/p>\n
While AI-driven automation is a key component, human-AI collaboration is equally important. Monitoring and management systems should empower operators and decision-makers with actionable insights derived from AI analysis. This collaborative approach minimises the risk of errors and enhances decision-making processes. Additionally, it allows human operators to focus on strategic tasks while AI handles routine monitoring, creating a synergistic relationship that maximises operational resilience in the digital era.<\/p>\n
Sustainability<\/h3>\n Incorporating environmental considerations into digital infrastructure management involves optimising energy usage, reducing carbon footprints, and implementing eco-friendly technologies and processes.<\/p>\n
AI in condition-based maintenance can play a pivotal role in this by identifying opportunities to enhance energy efficiency, tackle resource wastage, and contribute to the overall efficiency of operations and to the circular economy.<\/p>\n
<\/p>\n
Environmental responsibility also extends to the energy sources powering critical infrastructure. By leveraging AI in the data lake, organisations can analyse power consumption patterns and explore opportunities to integrate alternative energy sources. This not only aligns with eco-friendly initiatives but also enhances operational resilience by diversifying the energy mix and reducing dependence on conventional power grids.<\/p>\n
By its very nature, AI needs an enormous amount of data to learn and evolve, so as critical infrastructure grows, more data will be available for AI to use. At the same time, condition-based maintenance will naturally scale. This means that the more dense and varied the data population (thanks to new intelligent equipment and technologies), the better the data trending, pattern recognition, and insight learning will be.<\/p>\n
The technology evolution driving new product design that incorporates the enablement of data communication as a critical driver will enable algorithms to scale, thanks to enhanced data breadth and quality of the data. Through algorithm evolution, the precision of the data trending can continuously improve, enabling better learning.<\/p>\n
As technology continues to evolve, AI will undoubtedly play an even more significant role in shaping the future of the digital infrastructure.<\/p>\n
By embracing these advancements, a landscape can be created that is more environmentally friendly, resilient and capable of meeting the growing demands of the digital age.<\/p>\n","protected":false},"excerpt":{"rendered":"
Alex Brew, Regional Director, Northern Europe at Vertiv, discusses how AI enhances the efficiency and reliability of digital infrastructure.<\/p>\n","protected":false},"author":22,"featured_media":46819,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[570,885],"acf":[],"yoast_head":"\n
Digital infrastructure: How AI can help improve efficiency<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n\t \n\t \n\t \n