What is predictive maintenance?

Unlike other maintenance approaches that only repair assets at fixed intervals or after they fail, predictive maintenance leverages technologies like artificial intelligence (AI) and the Internet of Things (IoT) to detect early warning signs. This way, it is enabling maintenance teams to take corrective action.

Predictive maintenance programs rely on IoT sensors and IoT devices affixed to assets to gather data from assets. The data is then analyzed by using advanced AI and machine learning (ML) algorithms to identify changes in operating conditions and performance. When performed correctly, predictive maintenance helps teams identify patterns that lead to breakdowns and equipment failures.

Predictive maintenance solutions are widely used across many industries, including energy, transportation, manufacturing and mining, to streamline operations, reduce outages and increase asset lifespans. Unlike some other traditional types of maintenance, predictive maintenance strategies focus on condition-based maintenance (CBM). CBM uses sensors to detect when maintenance is required rather than simply adhering to a rigid schedule.

For example, in the manufacturing industry, maintenance teams use vibration analysis to monitor rotating equipment like pumps and compressors. They then feed the data from the vibration analysis into ML models to detect anomalies. Using this information, a computerized maintenance management system (CMMS) can automate maintenance scheduling, generate a work order and even assign a technician.

Predictive and preventive maintenance share the goal of reducing equipment failures and improving asset reliability through proactive measures, but there are several key differences worth considering.

• Preventive maintenance relies on maintenance schedules that are set up based on performance metrics and asset history. Operators and technicians perform regular inspections, lubrication and parts replacements according to a fixed calendar.

• Predictive maintenance enables real-time analysis of sensor data, enabling teams to repair and replace with less urgency and at a time that is strategic to the rest of the business.

While both approaches are more efficient than reactive maintenance in many ways, preventive maintenance still has inefficiencies that predictive maintenance doesn’t.

For example, in a preventive maintenance program, components are replaced on a schedule, even if they are in good working condition. In a predictive maintenance program, components are only repaired or replaced when the real-time data indicates its necessary.

Increasingly, predictive maintenance platforms are relying on AI and ML algorithms to analyze equipment conditions and performance and predict failures before they occur. AI-driven condition monitoring goes further than traditional condition monitoring by enabling systems to learn from the historical performance and maintenance data to improve recommendations.

AI predictive maintenance systems collect data from various sources, including IoT sensors, CMMS software, work orders, maintenance records and environmental monitoring systems. They then use AI models to uncover insights that might not have appeared otherwise. Modern AI systems help teams grow their maintenance discipline from an outdated reactive approach into a modern data-driven discipline.

Predictive maintenance relies on IoT sensors to collect data from machinery and equipment and analyze it using a CMMS. Machine learning algorithms give maintenance teams an accurate picture of an asset’s current condition and can send alerts when performance dips below a certain threshold.

Here’s a closer look at each of its five stages.

Data collection forms the backbone of all strong predictive maintenance programs. During this phase, organizations install IoT sensors on assets they plan to track that can then transmit real-time data about performance and operating conditions to a CMMS.

Variables collected during this stage include:

• Temperature
• Pressure
• Vibration
• Humidity
• Acoustic emissions
• Rotational speed

To detect changes in performance metrics, organizations must first set baselines for each of the assets they need to track. The baselines represent healthy ranges for performance during normal operating conditions.

Once a baseline has been set, a CMMS can automatically detect changes and recommend corrective action.

For example, if an engine typically vibrates within a specific range during operation, a deviation beyond a predefined threshold could indicate a potential problem like misalignment or imbalance. After collecting and analyzing the data over time, CMMS tools can recommend baselines based on performance and maintenance history.

The data analysis stage is essential to establishing an effective predictive maintenance practice. Once an organization has set appropriate baselines for the assets it needs to track, a CMMS equipped with predictive analytics can analyze incoming sensor data and help determine appropriate action.

During this stage, ML algorithms improve prediction accuracy over time, learning from the data the IoT sensors collect and helping technicians identify anomalies in performance. Advanced predictive maintenance solutions combine multiple data streams to improve reliability and reduce the likelihood of sending false alarms.

ML algorithms help identify a wide range of changes in conditions during this stage, including these examples:

• Increases or decreases in levels of vibration
• Inadequate lubrication
• Fluctuations in operating temperature or fluid pressure
• Electrical abnormalities

Another capability that sets predictive maintenance apart from other maintenance approaches is its automation capabilities.

Using predictive maintenance solutions, maintenance teams can automate work orders, inspection scheduling and even aspects of spare parts management. Automation capabilities help streamline workflows, improve response times and lower risks associated with human error.

Predictive maintenance can increase the value of enterprise asset management (EAM) practices to organizations by making the data gathered and analyzed by a CMMS available to leaders outside of the maintenance discipline. Advanced CMMS platforms can provide real-time visibility into asset health through intuitive dashboards that display metrics like risk scores, downtime and maintenance priorities.

There are five core types of analysis that underpin all predictive maintenance approaches.

Here’s a closer look at each one and the industries that primarily use them:

1. Vibration analysis: Vibration analysis is one of the most widely used predictive maintenance techniques, particularly for rotating equipment like pumps, compressors, motors, fans and centrifuges. Organizations in energy and utilities, manufacturing and transportation rely on vibration analysis to identify common problems like misalignment, imbalance and looseness of parts.
2. Oil analysis: Oil analysis is a predictive maintenance technique that evaluates the quality and extent of a machine’s lubrication and helps identify potential contamination issues. Technicians in the oil and gas, military and mining sectors must analyze lubricants for metal particles, water, changes in viscosity and chemical degradation. Also, oil analysis helps technicians monitor internal wear and tear without fully disassembling and inspecting a piece of equipment.
3. Thermographic analysis: Thermal monitoring is a predictive maintenance approach that is common in electrical systems and power distribution networks. Using thermal imaging cameras, predictive maintenance platforms can detect abnormal heat patterns in machinery that can lead to equipment failure.
4. Acoustic analysis: Acoustic analysis can detect both audible and inaudible (ultrasonic) noises that can be indications of mechanical defects. For example, air and steam leaks and bearing failures often produce distinctive acoustic signatures that predictive maintenance systems can detect before the underlying problem leads to equipment failure.
5. Motor current analysis: Motor current analysis is the process of evaluating electrical signals in machines to identify abnormalities. Motor current analysis can help detect a wide range of mechanical problems, including rotor issues, electrical imbalances, load variations and insulation failures.

Predictive maintenance offers significant operational and financial advantages for many enterprises. Here are some of the most common.

Predictive maintenance helps reduce unexpected equipment failures that cause unplanned downtime by helping organizations identify problems early and schedule repairs before breakdowns interrupt their operations.

In industries where unplanned downtime is both expensive and dangerous, this capability is especially valuable. For example, in the oil and gas sector, a single compressor or pump failure can have a ripple effect, shutting down platforms and even pipelines and halting production for months.

According to a recent report, unplanned downtime in the oil and gas industry costs hundreds of thousands of dollars per hour, on average. This means that it can exceed tens of millions of dollars per year.¹

Predictive maintenance helps organizations optimize maintenance costs by reducing unnecessary tasks and helping prevent catastrophic breakdowns.

A CMMS leveraging real-time data collected from IoT sensors can help teams perform the right tasks at the right time. This approach replaced repairing or changing components according to a schedule, regardless of their condition. According to a recent report, predictive maintenance reduces overall maintenance costs by 18% to 31% compared to traditional methods.²

For example, in the manufacturing industry, a worn bearing replaced during a planned maintenance window might cost a few hundred dollars. However, if it fails catastrophically, it could lead to more costly repairs and downtime. By using predictive maintenance techniques to anticipate breakdowns and take corrective action, teams use resources more efficiently, extend asset lifespans and reduce overall maintenance costs.

Predictive maintenance helps technicians increase asset uptime by identifying potential problems before they turn into equipment failures and force assets offline.

In certain industries like manufacturing, facilities and transportation, asset availability can impact an entire network. Using predictive maintenance practices, organizations can plan and control their maintenance activities and repair equipment while it’s still operational.

When used effectively and as part of an overall maintenance strategy, predictive maintenance can help improve productivity and operational continuity.

Real-time condition monitoring, a key part of an effective predictive maintenance strategy, allows organizations to maintain assets more effectively over time and reduce excessive wear and tear. By constantly tracking lubrication, alignment, temperature and other variables that impact reliability, predictive maintenance helps teams correct problems before they unnecessarily degrade a piece of equipment.

When assets are maintained predictively instead of through a traditional, reactive, run-to-failure approach, they have longer useful lives and their value to an organization increases. Proper lubrication, alignment correction and early fault detection are examples of predictive maintenance tasks that help extend asset lifespan.

Predictive maintenance solutions often require significant upfront investment. However, many organizations find they can achieve a substantial return on investment (ROI) through increased cost savings, reductions in outages and lower spare parts consumption.

Predictive maintenance strategies lower overall maintenance costs by proactively addressing issues before they cause catastrophic failures. Also, by tracking and monitoring assets throughout their lifespan, maintenance teams can strategically plan repairs, lowering their impact on core business operations.

Predictive maintenance has applications across all industries that rely on large, complex assets as part of their core business processes. Here’s a look at some of the most common use-cases by industry.

Advances in key technologies like AI automation, IoT, edge computing and virtualization shape the future of predictive maintenance. As machine learning algorithms become more sophisticated, organizations can collect and analyze larger volumes of real-time data more accurately. Predictive analytics platforms are also improving, gaining the ability to make complex decisions with minimal human input.

Edge computing, the distributed computing framework that makes IoT technologies possible, is also becoming faster and more capable. Instead of transmitting sensor data to the cloud, many edge devices can now analyze it locally in real time, reducing latency and improving responsiveness.

“Digital twins“ is another emerging trend that is likely to impact the future of predictive maintenance. A digital twin is a virtual representation of a physical asset that can constantly update itself using real-time sensor data. Organizations use digital twins to simulate real-world environments and test how their assets are likely to behave under certain conditions.

Ultimately, modern predictive maintenance capabilities represent a fundamental change in industrial asset management through the combination of IoT tools, machine learning, predictive analytics and real-time condition monitoring. Today’s most advanced organizations can move beyond fixed preventive maintenance schedules into more intelligent, proactive maintenance operations. As these capabilities become more accessible and cost-effective, their adoption will likely continue to grow from large enterprises to small and medium-sized organizations.