Using AI in Predictive Maintenance

When factory equipment fails unexpectedly, production slows or grinds to a halt. Time and money are lost, as is customer patience. Fortunately, manufacturers, utilities, energy producers, and other companies that rely on heavy machinery can use generative AI to predict machine failures more accurately than ever before. With this knowledge, they can schedule maintenance, avoid unplanned downtime, extend the lifecycle of expensive equipment, and ultimately help keep their production operations and supply chains humming.

What Is AI in Predictive Maintenance?

Predictive maintenance is a data-driven approach to predicting machinery failure and making proactive repairs. With the rise of the Internet of Things (IoT), the equipment used in smart factories, oil rigs, wind farms, electricity stations, mines, truck fleets, and other sectors is laced with data-gathering sensors that feed AI algorithms designed to monitor that equipment, detect anomalies, and prioritize maintenance.

Such systems continuously analyze operational conditions and look for signs that equipment may be in danger of failing, even if it seems perfectly healthy in the moment. By evaluating performance against baseline data, AI tools can flag even the smallest dips in efficiency, in real time, and prompt teams to open a maintenance ticket. As well as more accurately predicting when breakdowns will occur, companies gain a deeper understanding of the root causes of failure.

Key Takeaways

• Compared to older data analytics technologies, AI delivers faster, more accurate predictive maintenance.
• By using AI to predict machine failure and maintenance needs, companies can reduce downtime while boosting efficiencies.
• Some of the largest global businesses have deployed AI tools in their predictive maintenance programs, with promising results.

AI in Predictive Maintenance Explained

Manufacturers used to base their factory equipment maintenance schedules on projections of machinery lifecycles, including common failures. With AI, manufacturers can forego the guesswork by gathering and analyzing machine data to predict breakdowns, gaining a more nuanced view of individual machines and production networks.

They can also get maintenance recommendations in real time, with critical equipment first in line for fixes. One key benefit: While maintenance almost always requires some downtime, planning based on precise predictions helps keep that downtime to a minimum and schedules it for the most optimal periods.

Why Is Predictive Maintenance Important?

Two words: less downtime. Factories typically lose between 5% and 20% of their manufacturing capacity due to equipment failure and other causes of downtime, according to the International Society of Automation. Total downtime costs include decreased production, increased scrap rates, ineffective temporary fixes, and reliance on third parties to keep production rolling.

With so much on the line, it’s vital to predict machine health and maintenance needs accurately in order to reduce downtime. Per a 2024 Siemens study, the costs of an idle production line can add up. For large plants in the automotive field, stalled production can cost US$695 million per year, which represents a 150% increase compared to five years prior. The same study reported that the largest 500 companies globally lost 11% of their annual revenue as a result of unanticipated downtime.

Preventative Maintenance vs. Predictive Maintenance

Preventative maintenance and predictive maintenance are two proactive ways to monitor the health of factory equipment.

With preventative maintenance, companies evaluate their machinery at regular intervals, no matter how frequently or heavily the equipment is used. They typically draw from historical data and recommendations from their equipment suppliers to create rules-based maintenance schedules. The only variable is the length of time since the last evaluation.

While that approach is better than a purely reactive one, it relies on broad recommendations based on a narrow dataset. For example, it might recommend replacing an important (and costly) component without accounting for subtle factors that could suggest a longer life. Like reactive maintenance, over-maintenance can lead to avoidable downtime and expense.

With predictive maintenance, companies evaluate their equipment continuously using data that machine sensors feed to performance-monitoring software. AI algorithms analyze vast amounts of that data—including equipment temperature, vibration, pressure, and fluid levels—to build detailed models of equipment health and performance. As a result, the company can predict failure with greater confidence, while gaining more useful recommendations on what to fix and when. Unlike preventative maintenance, which is guided by less flexible rules, predictive maintenance uses real-time monitoring to respond dynamically and spot anticipated problems, root causes, and needed repairs.

One manufacturer specializing in injection molding uses predictive maintenance to detect and address anomalies in its robots and molding machines. By closely monitoring machine health and parts quality, the company reduces maintenance time, freeing employees to develop new products and improve operational processes.

Typically, companies use predictive maintenance to monitor machines whose failure would exact a steep toll in downtime, money, injuries, or lives. For example, if downtime in an electrical substation would leave thousands of people without power, the utility may choose to invest in finer-grained predictive maintenance, possibly leveraging AI tools. For lower-risk equipment not in critical paths, companies tend to stick with preventative maintenance, sometimes refining monitoring rules to gain nuanced data for more proactive maintenance scheduling.

Benefits of AI in Predictive Maintenance

Preventative maintenance and predictive maintenance are two proactive ways to monitor the health of factory equipment.

• Reduces costs
  More accurate predictions of machine failure can optimize maintenance schedules and decrease unplanned downtime and the associated costs. They also extend the life of equipment by flagging issues and recommending maintenance actions. AI algorithms can also closely track a machine’s energy consumption, detecting inefficiencies and suggesting steps to save money. They can even help trim labor costs by prioritizing maintenance work, thus reducing unneeded inspections, repairs, and replacements.
  
  One global manufacturer uses an AI system to monitor more than 10,000 machines, including robots, conveyors, drop lifters, pumps, motors, fans, and press/stamping machines. The manufacturer reports millions of dollars in savings, showing a return on their investment within three months of deployment.
• Limits disruptions
  AI reduces machine outages by predicting failures faster and more accurately than older methods. This helps manufacturers stay ahead of mechanical issues, increase their uptime, and keep their supply chains flowing.
• Increases production
  Predictive maintenance AI tools can help companies boost their labor productivity by 5% to 20%, according to a 2022 Deloitte study. One reason: AI can help reduce downtime by as much as 15%, keeping production lines rolling.
• Improves safety
  A failing machine can endanger workers. By knowing when machinery is likely to fail and preemptively making repairs, a manufacturer can avoid putting employees in harm’s way, including service technicians who often need to handle the equipment. US workers that operate and maintain machinery suffer about 18,000 injuries a year, according to the Occupational Safety and Health Administration. More than 800 individuals die per year.
• Extends equipment lifecycle
  By preventing premature wear and tear, AI-based data analytics tools can help prolong a mechanical asset’s lifespan, boosting manufacturer uptime, productivity, and, ultimately, revenue.
• Improves quality control
  Manual quality control practices are time-consuming and prone to human error. By automating equipment inspections and providing real-time feedback, AI tools help improve product quality and consistency, minimizing defect rates and reducing production costs. When AI algorithms are trained on massive amounts of product specification data, they can find cracks in products, misalignments, inconsistent colors and textures, and other issues. In monitoring torque, an AI system can track the consistency of torque values to ensure that bolts and screws are tightened to spec. If torque value goes beyond acceptable ranges, the system alerts the manufacturer to address the problem ASAP.

How AI Is Used in Predictive Maintenance

AI is powering predictive maintenance in sectors such as manufacturing, fleet management, package delivery, mining, recycling, and energy, all of which rely on sophisticated machinery. Companies can build automated models that monitor equipment conditions, detect anomalies, predict equipment failure and outages, prioritize and schedule maintenance, optimize energy usage, and recommend corrective actions.

• Outage prevention
  By gathering data, including historical performance and real-time contextual data, from connected assets, AI-based predictive maintenance reduces costly outages. Algorithms analyze data in real time and send reports to factory teams, flagging signs of potential failure—for example, overheated machines or improper voltage fluctuations.
  
  A large producer of aluminum deployed AI-powered tools to monitor robots and other equipment at smelting plants. Workers get maintenance warnings at least two weeks in advance, for example, on saw motors that underperform because of loose components. During each event, the company thus avoids 12 hours of unexpected downtime.
• Condition monitoring
  Manufacturers must monitor equipment conditions to keep things running smoothly. AI algorithms can recognize normal and abnormal conditions much faster and more accurately than older technologies. These systems analyze real-time data on equipment health, uncover relevant patterns, and predict failures to stay ahead of outages. They also help determine maintenance priorities based on in-the-moment conditions, not a predetermined schedule.
• Anomaly detection
  Until recently, anomaly detection was done with rules-based systems, which use hard-coded thresholds to spot machine irregularities and predict failure. For example, if a machine’s vibration exceeds the threshold set in ISO standards, the anomaly is reported and maintenance may be suggested. But complex equipment generates massive amounts of data, not only on vibration but also temperature, pressure, heat, and many other variables, making it harder for older systems to interpret information accurately.
  
  AI systems, by contrast, not only gather and analyze data but learn from it as they go. Instead of merely following rules and flagging current issues, AI-based analytics can identify even the faintest indication of performance deviation, sensing emerging problems before they cause disruptions.
• Failure prediction
  AI looks at historical performance and real-time sensor data to create a predictive model of equipment deterioration, helping companies avoid outright machine failure. As the model ingests more data, it learns, adapts, and predicts with increasing accuracy. Deep learning for fault prediction—a type of machine learning—finds links that elude older methods, including human observation. A multinational package delivery company uses an AI system to predict failure in more than 30 types of machines at sorting facilities, spotting gearbox failure, belt damage, and other costly problems. The company estimates that the system saves it millions of dollars annually.
• Planned maintenance prioritization and scheduling
  AI improves planned maintenance by pinpointing problems, identifying priorities, and reshuffling maintenance schedules, all in real time. Instead of focusing solely on current issues or creating schedules based on past data, AI-driven predictive maintenance reveals what’s likely to happen if equipment conditions remain the same. It also presents proactive steps that companies can take to produce better outcomes.
• Energy optimization
  As machinery becomes less efficient over time, it uses more energy to produce the same output. In the US, for example, inefficient compressed air systems waste $3.2 billion a year, according to the Compressed Air and Gas Institute. Using AI-based predictive maintenance, manufacturers and other machine operators can find equipment flaws and schedule repairs to prevent, or at least forestall, the need for scrap and rework. Ford Motor relies on AI to optimize energy throughout its plants, combining AI and digital twins to hunt down waste and pinpoint ways to eliminate it.
• Computer vision
  Computer vision technologies allow computers to see. For example, a manufacturer can train an AI tool to view and analyze videos that monitor machine conditions. Through visual refinement algorithms, images become information that complements other data by catching hard-to-detect anomalies such as subtle signs of wear and tear, misaligned parts, missing components, and even damaged packaging on shelves. Imagery also helps determine the root causes of problems and the most effective remediations.

Examples of AI in Predictive Maintenance

Some of the world’s largest manufacturers use AI to enhance predictive machine maintenance and improve uptime.

A global automaker uses AI to inspect and maintain welding robots in its factories. Specifically, it employs computer vision and deep learning to analyze images and videos of robots to spot defects. The AI system recommends parameters and settings for each robot and notifies workers when maintenance or replacement is required. The solution can reduce robot inspection time by 70% and improve welding quality by 10%, the automaker reports.

GE Aviation uses AI to predict the need for maintenance to its jet engines used by airlines and other customers. Some 44,000 engines have embedded sensors that feed data to GE monitoring centers in Cincinnati and Shanghai. GE combines the data with physical engine models and environmental details to predict maintenance issues before problems occur. Besides boosting engine reliability, its use of AI has reduced airline maintenance costs and enhanced safety.

Increase Uptime with Oracle Supply Chain Management

Improve supply chain resilience, reduce disruptions, and keep ahead of ever-changing market conditions with Oracle Cloud Supply Chain & Manufacturing applications. Use predictive maintenance tools in Oracle Fusion Cloud Maintenance, which embeds AI to boost visibility of machine performance while reducing downtime and operational costs.

AI in Predictive Maintenance FAQs

What is the role of AI in maintenance management?
AI can predict equipment failures and generate maintenance insights faster and more accurately than older technologies. In doing so, AI helps companies reduce machine wear and tear and unplanned downtime.

How can AI be used in maintenance?
Companies can use AI to monitor machinery conditions, detect anomalies, avoid equipment failure and outages, and prioritize and schedule maintenance.

How is machine learning used in predictive maintenance?
Machine learning algorithms can predict when factory equipment will deteriorate, fail, and require repair or replacement. They’re key to AI-driven predictive maintenance solutions.