10 min read
Artificial intelligence (AI) has become one of the most transformative technologies of our modern era.
AI promises to transform virtually all sectors and industries, but it’s perfectly suited to maintenance and facilities management, a discipline with a long-established relationship with technology.
By harnessing AI, facility managers can process and control information at scale, draw deep insights from data, and, ultimately, make better decisions.
The maintenance and facilities management sector is evolving rapidly, in part due to the integration of AI. The era of maintenance 5.0 takes advantage of intelligent software platforms, IoT sensors and AI models designed to automatically extract insights from complex data and harness it for real-time decision making.
Businesses are investing a sizable proportion of their operation budgets into maintenance and collecting more data via their sensors, which is creating large quantities of data that needs to be processed and analysed.
Complex facilities, such as oil rigs, might be equipped with as many as 500,000 sensors, which produce a stream of analysable data that can provide insights into everything from equipment performance to faults.
One of AI’s primary capabilities is working with complex data at scale. Through the amalgamation of sensors, big data, and machine learning algorithms, AI promises smarter facility operations.
By converting vast amounts of data into actionable insights, AI helps managers make informed decisions that can save time, money, and resources.
This includes real-time equipment monitoring, predictive maintenance, energy optimisation, and even improved safety protocols.
Maintenance and facility management is a multifaceted field with ongoing challenges. While these vary from industry to industry, some of the core challenges facility managers face include:
With expansive infrastructures and myriad assets, facility managers struggle to monitor every aspect of their operations closely.
Organising complex maintenance operations is often cited as a challenge. For instance, 85% of UK businesses view planning maintenance based on predictive signals as complex.
Stringent regulatory standards necessitate rigorous inspections, record-keeping, and maintenance schedules to avoid penalties.
Maintenance can account for up to 50% of operation budgets. Economical constraints often limit the resources available for maintenance activities, making it challenging to ensure top-tier facility operations.
Streamlining operations while ensuring optimal human resources and equipment output remains a top concern.
AI offers a multitude of solutions to the aforementioned challenges:
By integrating AI-powered monitoring systems, managers obtain real-time insights into the operational status of their assets.
Businesses often collect and create more data than they use. AI maximises the usefulness of extensive data, helping businesses extract meaningful insights from their sensors and monitoring systems.
AI can monitor and ensure facilities adhere to regulatory standards, notifying managers of potential compliance breaches.
AI can help predict when equipment will likely fail, or a part needs replacement, allowing for proactive maintenance and significant cost savings.
With AI-driven insights, operations can be streamlined, and resources deployed more efficiently, ensuring optimal productivity.
Predictive maintenance uses data-driven insights to predict when equipment or machinery might fail or require maintenance, enabling timely intervention.
Traditional methods often revolve around scheduled maintenance, which might not always be timely or efficient. Predictive maintenance, however, offers the ability to maintain equipment precisely when needed, preventing both unforeseen downtimes and unnecessary maintenance checks.
Some 48% of businesses already use monitoring devices to shift the emphasis from scheduled to preventive maintenance. A PwC report found that, while complex to orchestrate in practice, predictive maintenance decreased maintenance costs by 12% and increased asset lifetime by some 20%.
This results in enhanced equipment longevity, a sharp reduction in maintenance costs, and the assurance of smooth, uninterrupted operations.
AI takes predictive maintenance to the next level.
By ingesting and analysing vast amounts of data – ranging from machinery’s operating conditions to its historical maintenance records – AI can identify subtle patterns and anomalies that might escape human scrutiny.
For instance, an AI system might identify that a particular type of machinery often fails when operated above a specific temperature range for extended periods.
Such insights can lead to tailored maintenance schedules and early interventions, drastically reducing potential downtimes. Additionally, AI systems can adapt and learn from their inputs, becoming more accurate over time.
Real-time condition monitoring is an advanced application of AI in maintenance and facilities management.
Here, equipment and assets are outfitted with sensors that continuously collect data, from temperature and vibration levels to acoustics and moisture content.
AI systems process this steady stream of information, identifying potential issues or deviations that signal issues.
This data is collected and processed in real-time, with connections often facilitated wirelessly via 4G or 5G. Some data can be processed locally by edge devices.
The real boon of AI-enabled condition monitoring lies in its ability to detect issues well before they escalate into major problems.
For example, if left unaddressed, a minor vibration anomaly in a pump could lead to significant damage or even equipment failure. Early detection in such cases is paramount to cutting maintenance costs.
Real-time monitoring instantly flags such anomalies, allowing for immediate attention and resolution. This not only safeguards the equipment but also prevents expensive downtimes and replacements.
Preventive maintenance with early detection is associated with significantly lower downtime and greater productivity.
Efficient resource allocation is vital for the seamless operation of any facility. AI excels in this domain by analysing complex datasets, from employee schedules to equipment availability and stock.
Using this data, AI algorithms can forecast demand and ensure that the right resources are available at the right time, enhancing operational efficiency.
AI provides a balanced approach by determining the optimal time for maintenance activities without hampering ongoing operations.
For instance, AI could determine that a particular machine, though due for maintenance, can still operate efficiently for a period without increased risk.
This kind of predictive decision-making ensures that there’s minimal disruption to the daily operations of a facility. It’s about reducing downtime and balancing maintenance to ensure the highest productivity possible, all while cutting costs and energy.
Data is the lifeblood of AI systems. Without it, predictive models and algorithms can’t function effectively. Data from sensors, logs, and other sources provide invaluable insights that algorithms analyse to deliver insights and decisions.
These insights enable managers to foresee potential issues, optimise operations, and enhance the lifespan of assets.
While gathering data is essential, its true value lies in effective analysis and interpretation.
AI-driven maintenance management software and Intelligent Maintenance Management Platforms (IMMPs) sift through vast data sets to extract actionable insights.
This vastly simplifies the process of harnessing AI for maintenance purposes. IMMPs integrate data from different sources and make it available in easy-to-read, intuitive formats. Minimal convoluted manual processing or data analysis is required.
Ultimately, IMMPs help maintenance managers identify patterns, anomalies, and trends, equipping facility managers with the knowledge to make informed, strategic decisions.
AI combined with augmented reality (AR) has been highlighted as one of the most transformative trends influencing the sector over the last few years.
AR enables novel, cutting-edge maintenance operations. For instance, a technician equipped with AR glasses can visualise real-time data overlays on equipment, aiding in diagnosis and repair.
There are already many working examples of AR in action, such as using AR overlays to help technicians troubleshoot and service complex PCB circuit boards.
Additionally, AR can simulate maintenance scenarios, guiding technicians step-by-step through complex procedures.
Training new staff can be resource-intensive and time-consuming. However, with AI-powered AR applications, newcomers can be quickly brought up to speed with immersive training experiences.
Additionally, AR can highlight potential problem areas during inspections, ensuring nothing is overlooked.
Further, AI-driven AR can provide instant solutions in troubleshooting scenarios, reducing downtime and improving efficiency.
Maintaining compliance with ever-evolving safety regulations can be challenging. AI eases this burden by constantly monitoring operations and ensuring they align with the latest standards.
If discrepancies arise, AI systems can instantly alert facility managers, allowing them to rectify issues before they escalate into regulatory violations.
Safety is paramount in facility management. Advanced AI systems are adept at identifying potential risks, such as overheating machines, electrical overloads, or excessive friction or vibrations.
AI can preemptively identify and address hazards by continuously analysing data and comparing it against safety benchmarks, ensuring a safer environment.
The integration of AI in maintenance and facilities management is not without its challenges:
As facilities become more interconnected, there’s an increasing amount of data being generated and analysed. Ensuring this data remains private, especially if it includes information about occupants, is paramount. One study found that 89% of businesses were concerned about data privacy.
With the integration of IoT devices and AI-driven systems, there’s a larger digital footprint, making facilities potentially susceptible to cyber threats. Robust cybersecurity measures are essential to protect both the data and the infrastructure.
Implementing AI-driven solutions requires a shift in how employees approach their tasks. There’s a learning curve involved, and ensuring the workforce is adequately trained and comfortable with these new tools is vital for successfully adopting AI.
The landscape of AI in facilities management is ever-evolving:
AI models are being developed that can predict when something might fail and take preventive measures, like adjusting machine operations or even initiating minor repairs.
Beyond just maintenance, we are moving towards wholly AI-driven buildings, where lighting, heating, cooling, and security are all controlled optimally by AI, adapting in real-time to the occupants’ needs.
Facility managers could soon have AI assistants, similar to Siri or Alexa, specialised for their roles. These AI systems could process queries, provide insights, and even execute tasks upon command.
Looking ahead, AI’s role in maintenance and facilities management is only expected to grow:
We might soon see robots patrolling facilities, carrying out inspections, and performing maintenance tasks – all under the guidance of AI.
AR technology will enhance its symbiotic relationship with AI as it becomes more advanced. This could lead to real-time 3D visualisations of a building’s infrastructure, allowing for unprecedented insights into its workings.
I will work alongside facility managers instead of replacing human roles, offering data-driven insights and solutions. The human element will always be essential, but decision-making will become more sophisticated with AI’s assistance. This will unlock accurate predictive maintenance, excellent energy conservation, and near 100% uptime.
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