Predictive maintenance (PdM) is a proactive, data-driven maintenance strategy for reducing downtime and extending asset lifespan. Done right, predictive maintenance increases operational efficiency and boosts reliability, while lowering overall maintenance costs.
This article will explain exactly how predictive maintenance works and describe some real-world examples. We’ll also talk about how AI tools can scale and expand PdM maintenance programs.
As the name suggests, predictive maintenance helps MRO teams predict exactly when their critical assets will need repairs.
Predictive maintenance systems collect asset health data and analyze it for signs of a new or developing machine fault. Changes in an asset’s vibration levels, heat signature, or oil quality can all indicate a defect. By paying careful attention to these indications, and analyzing them with the right tools, MRO teams can successfully predict upcoming maintenance needs.
Predictive Maintenance vs. Preventive Maintenance: What's the Difference?
Predictive maintenance and preventive maintenance are both proactive maintenance strategies. Both are effective ways to prevent asset failure, reduce unplanned downtime, and keep assets in optimal condition. However, the two approaches are very different.
Preventive maintenance is a calendar-based approach. Maintenance teams clean and lubricate parts, replace belts and valves, and perform inspections on a fixed schedule.
Preventive maintenance reduces unplanned downtime and keeps equipment in excellent condition. However, it is labor-intensive and can be a drain on resources. Teams sometimes over-maintain assets, leading to premature wear and tear. There’s a lot of scheduled downtime to perform maintenance tasks. And, since inspections are calendar-based, it’s possible to miss asset faults that develop between inspections.
Predictive maintenance delivers lower costs and more efficiency, with less planned downtime. Sensors track machine health in real-time, identifying exactly which assets need maintenance. That means no more wasted resources, overworked teams, or wasted spare parts. Instead, teams perform maintenance when it’s needed.
PdM tools monitor asset health on a continuous basis, so teams stay on top of every new machine fault. Predictive maintenance gives you insight into the future, so that you can set maintenance priorities and schedule maintenance tasks based on your circumstances, instead of following a calendar.
How Does Predictive Maintenance Work?
Predictive maintenance uses a network of wireless sensors to collect condition monitoring data on a constant basis. The sensors, mounted on assets or components, track vibration levels, temperature, and other key metrics during machine operation. They stream that data to the cloud for analysis in real-time.
Analytic tools scan the condition monitoring data for any changes from the machine’s normal operating baseline. When there is an anomaly, the software alerts technicians to carry out inspections or repairs. A good CMMS, like eMaint, can auto-generate work orders so that teams never miss an alert.
Most of the time, predictive maintenance strategies allow managers plenty of lead-time so that repairs can be scheduled whenever is most convenient – like during a planned shutdown.
PdM in 2025: Benefits and Challenges
Modern plants need predictive maintenance strategies more than ever. Most operations today are dealing with significant labor shortages and budget constraints –they’re also facing stiff competition and tight deadlines.
Predictive maintenance gives managers the tools to meet production goals, extend asset lifespans and reduce downtime, even with a limited team. PdM will also reduce overall maintenance costs.
However, PdM in 2025 has some built-in challenges. The typical plant today generates so much data that there’s just not enough time to analyze it. All too many organizations are drowning in data instead of drawing insights from it. Many managers say they want to shift to predictive maintenance, but they don’t know how to successfully implement the approach with their limited resources.
That’s where Industry 5.0 technology comes in.
Industry 5.0 tools, like automation and AI, make it possible for lean teams to analyze reams of condition monitoring data, assess overall plant health, and diagnose machine faults. State-of-the-art AI systems like Azima DLI can diagnose more than a thousand distinct machine faults and create detailed guidelines for maintenance crews to follow. It’s predictive maintenance at its best: digital tools and data-driven insights that allow people to do their own work more effectively.
Types and Methods of Predictive Maintenance
There are many different predictive maintenance technologies. Each organization has its own unique set of needs, and it’s a good idea to tailor your PdM program to meet those needs.
Vibration monitoring
Wireless vibration sensors measure and record asset vibration levels in real-time. Once the machine’s baseline is established, sensors track the asset’s ongoing vibration levels and issue alerts when there’s an anomaly. Changes in vibration patterns can indicate misalignment, bearing faults, looseness, or unbalance.
Temperature monitoring
Sensors mounted on or near assets measure deviations from a machine’s baseline temperature. Changes in temperature can indicate leaks in boilers or HVAC systems, faults in electrical connections, or excess levels of friction.
Power monitoring
Power monitors track and record the subtle fluctuations in current, voltage, frequency, and energy consumption that can indicate a developing fault in an electronic asset. Catching these issues early reduces wear and tear on assets, extending their useful life.
Oil Particle Monitoring
Excess particles in machine oil can indicate that the machine is experiencing premature wear and tear. High levels of oil particles can also indicate a problem with filtration or ingression. Oil particle counters monitor oil purity on a continual basis, alerting you to the first signs of trouble.
Predictive Maintenance Examples
Predictive maintenance reduces downtime and increases operational efficiency in just about every sector. Here are a few predictive maintenance examples:
Predictive Maintenance in Automotive
As automotive plants scale and adopt new technology, MRO teams are adapting by scaling their PdM programs. Wireless sensors monitor conveyor belts throughout the plant for early signs of failure. Sensors track changes in vibration levels from stamping and press machines, as well as forklifts and painting equipment.
Predictive Maintenance in Food and Beverage
Predictive maintenance can improve food safety and make compliance easier by ensuring that critical assets are maintained in peak condition. Blowers, mixers and blenders, dust collection systems, pumps and conveyor belts all benefit from a predictive maintenance approach.
Predictive Maintenance in Manufacturing
Well-maintained machines produce uniform, high-quality goods and materials. A predictive maintenance approach ensures that high-cost, complex equipment stays in peak condition. Presses, rollers, manufacturing robots, and large motors are just a few of the assets that benefit from predictive maintenance. PdM also dramatically reduces downtime and production slow-downs.
Predictive Maintenance Analytics
Predictive maintenance uses data analytics to deliver targeted, actionable insights on the health of every asset in your plant. PdM leverages failure histories, condition monitoring data, and work order history to assess equipment health and make predictions about future maintenance needs.
Today, many organizations are using AI-powered tools for data analytics. AI has the power to “read” vast quantities of data from different sources, synthesize it, and analyze it for meaningful patterns.
Today’s AI goes far beyond simple band alerts and notifications. The leading AI tools, like Azima, can perform root cause analysis, diagnose issues, and determine fault severity. AI can also put machine data in context and issue detailed “prescriptions” for maintenance teams to follow.
Predictive Maintenance Services
Predictive maintenance is widely recognized as one of the best ways to reduce downtime, lower costs, and increase operational efficiency. However, a lack of in-house expertise holds many organizations back from implementing a PdM program. That’s why predictive maintenance services are so important.
Depending on organizational needs, it can make sense to outsource condition monitoring, asset health assessments, and long-term maintenance priority setting. Predictive maintenance services can include:
- Vibration analysis
- Remote condition monitoring
- Periodic condition assessment
- Remote expert on-demand
- Repairs and calibration
- Training and ongoing education courses
Today, many businesses are using a combination of AI and outsourcing to analyze condition monitoring data.Tapping into PdM services is a great way to extend the reach of your workforce, maximize your resources, and ensure that your plant functions at peak performance.
Best Practices for Building a Predictive Maintenance Program
Whenever you implement a new maintenance program, it’s a good idea to start small. Pick out a limited number of assets and create a pilot program with a small, dedicated team and clearly-defined benchmarks.
Before starting, make a clear plan. List the predictive maintenance tools and methods you will use, whether that means vibration sensors, power quality monitors, or oil counters. Set a clear target and goals for your pilot.
Choose your team carefully and provide plenty of training in predictive maintenance techniques. Make sure your team understands the strategy and goals, and set up a system for data collection and data sharing. A CMMS, like eMaint, provides robust reporting and analytics, and facilitates data sharing even when some team members operate remotely.
Finally, use your data to measure the pilot’s success and to analyze areas that need improvement. Share the data with key members of your organization so that you can move forward to implementing the program more broadly in your plant.
Predictive Maintenance with Fluke Reliability
Fluke, Azima DLI, and Pruftechnik, all part of the Fluke Reliability family, build the hardware and software that drives today’s best predictive maintenance programs. Our team of experts also provides tailored services that meet each organization’s individual needs.
Whether you’re interested in starting a predictive maintenance program, or you’re troubleshooting your current maintenance strategy, our experts can help. We provide remote and on-site assistance with implementation, as well as fully remote condition monitoring, root cause analysis, and troubleshooting. Our team also provides training and ongoing education courses to help teams get the maximum benefit from their predictive maintenance programs – so that you can achieve greater uptime, increased productivity, and more operational efficiency.