La función de la tecnología de sensores en la revolución de la mantención preventiva.

Artificial Intelligence in Maintenance: Ushering in a New Era of Efficiency and Reliability

Artificial intelligence (AI) has successfully moved from an unreachable concept seen only in science fiction movies to an everyday reality in our modern world. Where the next step takes us, whether it be sentient robots running repairs or self-fixing machines doing all the work, we won’t know until that time comes. 

However, we do know about the substantial benefits of using artificial intelligence in maintenance today. 

Today, AI is commonly discussed and broadly defined, touching everything from education to industry. The maintenance and reliability sector is no different, working to determine how and where AI technologies can make the biggest impact.

In this article, we will discuss how AI has benefited the maintenance realm and share a few secrets for using this technology to improve your operations. 

Why Is AI Reliability for Maintenance Important?

Before discussing how AI is revolutionizing maintenance and reliability, it’s important to understand the state of the industry. 

Maintenance has historically been seen as a necessary evil, a cost center that was simply required to address breakdowns and other problems with machines and equipment in the manufacturing world. 

It’s estimated that 70–80 % of the costs of a facility asset are realized during the operation and maintenance phase of the facility life cycle. That’s a lot of opportunity to preserve your overhead with solid maintenance practices or generate phenomenal amounts of waste by not scheduling activities enough or, just as bad, too much. 

If you’re one of the 61% of maintenance facilities polled by Plant Engineering, most maintenance tasks are reactive — performed only when assets fail — and often communicated manually through conversation, notes, voicemail, and email messages.

How Is the Evolution of AI Impacting Maintenance Practices?

Advancements in technology have offered another, less analogous route. 

Over the last decade, computerized maintenance management systems (CMMS), along with the concept of reliability, have moved maintenance practices toward preventive ones. Organizations understand the high costs of reactive maintenance and the resulting downtime. CMMS technology has helped many manufacturers schedule inspections and maintenance tasks based on time or usage, improving reliability or the chance that machines will perform as expected.

UpKeep is a revolutionary tool that combines the power of CMMS and EAMs and supplements it with AI technology. 

We understand how many variables go into running your maintenance endeavors. By offering automating simple tasks, you can have more time to turn your attention to other critical matters. Learn more about our product or schedule a free demo today. 

How Is Artificial Intelligence Used in Maintenance?

While it would be interesting to have sentient robots repair and monitor assets automatically, like in a sci-fi movie, we’re not quite there yet. Artificial intelligence in maintenance today mostly focuses on the organizational aspects of maintenance practices. Let’s review some of the maintenance areas that AI is currently improving. 

Preventive Maintenance (PM)

PM is a scheduled maintenance practice designed to minimize failures by creating a set of routine tasks, such as calibrations, inspections, and replacements, to be performed at regular intervals. While preventive maintenance is normally conducted without AI, CMMS tools often use it to streamline operations, automate workflows, and increase productivity. 

Request a Demo

Total Productive Maintenance (TPM)

TPM aims to improve operations by having all staff participate in maintenance tasks, whether menial or substantial. This practice might include all front-line staff, equipment operators, managers, and engineers. The idea is to reduce resource allocation by involving more people in maintenance efforts, thereby improving maintenance operations and reducing failure risks. 

AI tools and a powerful CMMS allow maintenance managers to monitor maintenance practices and assign tasks with ease. 

Predictive Maintenance

AI-driven predictive maintenance services build on TPM and PM. Rather than only focusing on current issues or creating schedules based on historical data for ongoing maintenance, predictive maintenance anticipates potential  problems if current conditions persist or how changes could impact operations if the issue is not remedied.

Using a CMMS supported by AI and other integrated technology may enable facilities to better handle proactive measures that address issues before they arise.

9 Ways AI Can Revolutionize Your Maintenance Practices and Reliability

From helping facility leaders optimize their current maintenance practices to automating processes for better resource allocation, AI is the natural next step in improving maintenance and reliability. 

When it comes down to the main point: AI can help companies better predict when a critical asset is most likely to fail, allowing the maintenance team to prioritize and schedule repairs and tasks most efficiently. But that’s not all artificial intelligence can do for your facility. 

Here are a few other key areas where AI can positively impact the reliability of your maintenance practices: 

#1: Automation of Work Orders

Using a CMMS supplemented with AI capabilities, you put your maintenance tasks on autopilot as your work orders are generated automatically. So, instead of having to manually create every work order (even for items that reoccur daily), UpKeep’s intuitive CMMS will create and submit work orders based on the following:

• Pre-scheduled tasks

• PM checklists

• Triggers picked up by Internet of Things (IoT) sensors

UpKeep also allows you to set parameters for work order generation based on the following:

• Predefined events

• Job and asset types

• Technician skill set

• And more

This means your work orders will always be generated accurately and sent to the right technicians. 

With an automated workflow, you won’t have to spend time on repetitive daily tasks. Now you can turn your attention to more important matters and increase your productivity. 

#2: Asset Health Monitoring

Another significant factor in AI maintenance is the use of IIoT sensor technology. Sensors are programmed with set parameters and attached to measure various KPIs, including:

• Temperature 

• Humidity 

• Vibration 

• Water levels

• Energy usage 

These sensors monitor asset health and trigger alerts when measurements fall outside their defined parameters. Monitoring your equipment and assets around the clock means you can strengthen your predictive and preventive maintenance capabilities. But artificial intelligence takes this a step further. If certain data falls outside an acceptable range, this can indicate an impending equipment failure.

By using maintenance history data, such as previous work orders and readings, AI may be able to spot a trend and trigger preventive maintenance before the IoT sensors signal a potential failure. 

The maintenance team can be alerted immediately, and a work order can be scheduled to prevent or minimize the resulting downtime or product losses.

One of the most illustrative examples is in the food industry where ingredients and finished food products need to be kept at safe temperatures. Suppose a sensor can monitor the temperature of these storage units around the clock and immediately send an alert once those temperatures are out of an acceptable range. In that case, it eliminates the chance of losses due to spoilage. Predictive maintenance is much more effective than periodic inspections of freezer or refrigeration temperatures.

#3: Work Order Prioritization

When you think of how AI can optimize your maintenance scheduling, you probably consider what we already mentioned: automated work order generation and IoT sensors.

While it’s true that the main focus of artificial intelligence in maintenance is those two key abilities, we tend to forget that work order prioritization and approval are other tedious tasks involved in the maintenance scheduling process. 

With UpKeeps AI-backed CMMS, you don’t have to worry about calculating your asset’s priority risk number and dividing your resources to ensure your priority asset’s needs are met. 

Our comprehensive maintenance management software uses AI to gather data and prioritize tasks based on the predicted likelihood of failure, the criticality of the equipment, and available resources. This allows your PdM and PM activities to be suitably assigned. 

#4: Failure Prediction

Through asset health monitoring and centralized data collection, AI can accurately predict onset failures and allow maintenance teams to proactively schedule planned downtime and approach the problem before loss of product or wasted time. 

This is just another feature of a manufacturing ecosystem balanced and nurtured by AI-assisted CMMS. If an asset is flagged for impending breakdown, AI can help you appropriately allot resources to handle the problem in its infancy. 

#5: Schedule Optimization

Artificial intelligence in maintenance means you have total oversight of your operations. You know which machines are high priority, what trends may signal impending repairs or inspections, how your assets are performing, and more. 

All of this insight means you know how to optimize your maintenance schedule to maximize productivity, minimize waste, and reduce downtime. 

Request a Demo

#6: Inventory Management

AI prevents stockouts and overstocking, forecasts demand fluctuations and enhances supply chains through data analysis and machine learning algorithms. By leveraging AI in inventory management, facilities can improve decision-making, boost efficiency, and maintain optimal asset repair inventory levels.

For example, say you’re running low on a specific part essential for extensive repairs. To avoid a stockout, the AI assisting your CMMS will automatically order that part so you avoid a scenario where that part is needed for a critical repair but you’re out of it. 

#7: Better Allocation of Resources

The bottom line is that AI can help an organization make better use of limited resources, whether that be people, materials, time, or energy. Maintenance has always been tricky to balance — too much maintenance means wasted effort and money working on assets that are performing fine, while too little can mean costly breakdowns and downtime.

AI is helping manufacturers get closer to just-in-time maintenance and repair by analyzing vast amounts of data to determine the most optimal time to perform maintenance tasks, conduct inspections, make repairs, or replace equipment.

By doing so, technicians are working on the most important tasks, supplies such as parts or lubricating oil are not wasted, and energy is spent wisely. All of these things mean lower costs for the organization and a more responsible use of the world’s limited resources to be a sustainable member of the community. 

This  also positively affects a company’s bottom line, helping to maximize profitability as well as enhance a business’s reputation as an environmentally responsible, customer-focused organization that delivers quality products and services reliably.

#8: Enhanced Safety

Employee safety is a top concern for many businesses, and AI can help enhance working conditions by ensuring that equipment is functioning properly and safely.

By analyzing vast data, AI can better predict when a critical piece of equipment may become a safety hazard, alerting the maintenance team to make necessary repairs or replacements before an incident occurs.

For example, boilers generating heat in manufacturing can be dangerous if leaks or explosions occur. Data collected from manufacturers, historical maintenance records, and IoT sensor information can be used to determine when a boiler may be at risk for malfunction.

#9: Boosted Customer Satisfaction

It’s clear that today’s consumer is more demanding than ever with rising expectations for high quality, fast delivery of products and services as well as round-the-clock assistance through multiple communication channels.

AI can help a company better meet these expectations by ensuring that production lines are optimized and delivery schedules are met or exceeded. If AI can help improve maintenance and reliability, a natural consequence is decreased downtime. Products can then be delivered on time to waiting customers.

Wield the Power of AI for Your Maintenance Processes With the Help of UpKeep

UpKeep’s CMMS solution was created to help organizations establish and achieve effective facilities management goals in any way possible. We found that offering users the opportunity to cut time spent on menial managerial tasks with the help of AI was one of the best ways we could do this. 

Unlike other AI-assisted maintenance management software, our technology promotes a digital ecosystem that allows you to manage the areas you want and automate the rest. Through our countless integrations and customizable data hubs, we help our users create personalized maintenance practices for their  facilities and management styles. 

With the help of UpKeep’s AI-assisted technology, you'll be able to strengthen your maintenance operations, helping you:

• Say goodbye to paperwork with our software’s seamless collaboration and real-time updates for work orders within and across locations.

• Access information at the touch of a finger (on the go with our mobile app). Your entire asset history, from maintenance history to warranties and depreciation, is in your back pocket wherever you are. 

• Make data-driven decisions by using real-time data and advanced analytics to help you optimize asset performance.

• Track assets in real-time with wireless sensors, making it easy to monitor assets remotely. This takes preventive maintenance to the next level.

Ready to get started? Learn more about our tools or request a demo today.

Request a Demo