What Is Predictive Maintenance? Types & Examples

It’s said that no one can accurately and consistently predict the future — but that doesn’t stop companies from trying. When it comes to maintaining critical manufacturing equipment and components, many are adopting predictive maintenance strategies in which they initiate equipment repairs — before machines fail —in response to constant monitoring and analysis of the equipment’s behavior. The goal is to prevent failures before they happen, which eliminates downtime and lowers maintenance costs. And since research has shown that the cost of unplanned downtime, alone, is measured in the trillions of dollars per year, the potential savings from predictive maintenance is large.

The key to a successful predictive maintenance strategy is understanding the process and how to implement it. This article delves into the key elements of a predictive maintenance strategy, how it works, how it compares to other forms of maintenance and the benefits companies can expect. It also looks at common challenges and a few examples of predictive maintenance in action.

What Is Predictive Maintenance?

Predictive maintenance is a strategy to proactively detect potential defects and anomalies in equipment behavior before they cause failures. It relies on technology that collects and interprets data during regular equipment operations to forecast wear and tear. Predictive maintenance also helps companies build maintenance schedules that extend the equipment’s useful life while reducing downtime.

But predictive maintenance isn’t just about preventing downtime. It’s also about improving operational reliability and efficiency by shifting to a more proactive maintenance strategy — a crucial outcome for industries where equipment failure means expensive stoppages and lost productivity.

Companies typically implement predictive maintenance by installing sensors and using advanced analytics to monitor equipment in real time. The sensors collect data about various operational conditions, such as vibration, temperature and pressure. The data is then analyzed using advanced algorithms that predict when a piece of equipment will likely fail so that maintenance can be planned before an outage occurs. The result is reduced operational costs, improved asset management and a more predictable maintenance budget.

For many companies, the need for predictive maintenance couldn’t be more urgent. According to a 2023 report from Siemens, Fortune Global 500 companies are losing a staggering $1.5 trillion annually due to unplanned downtime, an increase of 8% compared with just two years earlier. Reversing that trend is driving the predictive maintenance market to new heights: It grew at a 7.6% compound annual growth rate from 2019 to 2023 and is expected to grow at 10.9% per year from 2024 through 2034.

Predictive vs. Preventive Maintenance

Predictive and preventive maintenance both aim to improve equipment reliability, but they’re ultimately different approaches suited to different operational contexts. Preventive maintenance is scheduled based on time or usage, such as lubricating a machine every 30 days. It’s a systematic, simple method to implement that ensures routine upkeep and helps reduce sudden failures. But it can also lead to unnecessary maintenance because it doesn't consider the actual condition of the equipment. As a result, companies can sometimes waste resources and experience downtime for equipment that’s still fully functional. It can also lead to unnecessary parts replacements.

On the other hand, predictive maintenance relies on real-time data to anticipate equipment failures before they happen. Sensors and advanced analytics predict when to perform maintenance based on the actual condition of the equipment, which can greatly reduce unnecessary maintenance and extend machine life. The challenge of predictive maintenance, however, lies in its complexity and the initial investment in technology and training required to interpret the data.

Choosing between these maintenance strategies depends on several factors, including the importance of the equipment, the cost implications of downtime and the resources available to invest in and manage advanced predictive technologies. For example, industries like aviation or energy, where equipment failure results in significant safety risks or major production losses, might prefer predictive maintenance for its real-time monitoring capabilities. In industries where precise time-to-failure predictions are less critical, companies might opt for the simplicity and reliability of preventive maintenance.

Key Takeaways

• Predictive maintenance aims to repair equipment before it fails through constant monitoring and analysis of its behavior.
• It uses real-time sensor data and advanced analytics software that uses predictive modeling to monitor equipment wear and tear.
• Common predictive maintenance methodologies include vibration analysis, motor circuit analysis and oil analysis.
• The key benefits of predictive maintenance include decreased downtime, lower costs, increased equipment longevity and greater safety.
• Implementing a predictive maintenance strategy requires upfront costs, technological and analytical expertise and integration capabilities.

Predictive Maintenance Explained

Predictive maintenance grew out of companies’ desire to break away from more costly maintenance strategies. For example, reactive maintenance takes action only after equipment fails, which usually means more costly repairs or replacements. Emergency work and/or expedited parts and labor costs, which often come at a premium, are required to avoid or at least minimize unplanned downtime that hampers productivity. Another alternative, preventive maintenance, initiates interventions at fixed intervals, which is often more cost-efficient than reactive maintenance but can still lead to unnecessary maintenance activities and costs.

Predictive maintenance, however, leverages data-driven insights to forecast equipment failures before they happen, so interventions are both timely and effective. Its popularity is rising as key technologies come together: Internet of Things (IoT) devices that gather real-time data from equipment; machine learning (ML) and advanced analytics, which interpret data to predict failures; and cloud computing, which allows companies to store and process large datasets efficiently.

How Predictive Maintenance Works

Effective predictive maintenance requires a combination of technologies and skills, with sensors at the heart of the process. Companies usually look for solutions that can be integrated with existing infrastructure, since replacing manufacturing gear and related information systems would be extremely expensive. Scalability and ease of use are also critical when looking for analytics software. The following steps outline how the predictive maintenance process typically works.

The Predictive Maintenance Process

1. Equipment Selection and Sensor Installation

   Selecting the right equipment to monitor and installing the necessary sensors are vital first steps in the predictive maintenance process. Not all assets are equally important — or suitable — for predictive maintenance. Predictive maintenance should focus on high-value equipment, such as a gas turbine in a power generation plant, whose failure can cause significant operational disruptions or safety risks. It’s important to identify equipment that frequently fails or has high repair costs and fit them with appropriate sensors designed to continuously gather data on operational metrics, such as vibration, temperature, pressure and sound.

   Installing these sensors correctly is also crucial for capturing accurate data. Sensors must be precisely positioned on the equipment’s critical components, then calibrated and tested regularly to maintain accuracy and reliability.

2. Data Collection and Transmission

   Data collection and transmission provide the foundation for forecasting that leads to timely interventions. It’s particularly important to select the right types of sensors and position them correctly to capture the most relevant data for finding early signs of wear or failure. The precise sensors to use depend on the machinery and metrics being monitored. Common choices include vibration sensors for rotating equipment, temperature sensors for overheating risks and acoustic sensors for unusual noise detection in mechanical systems. Proper positioning of the sensors typically requires expert guidance.

   Once collected, data should be transmitted securely to a central system for analysis. Advanced communication technologies, like IoT networks, typically support a seamless flow of data in real time, allowing the predictive maintenance system to analyze trends and predict breakdowns before they occur.

3. Data Processing and Analysis

   Once raw data from sensors arrives in the central system, data processing and analysis take the stage to transform the data into a usable format for identifying patterns and trends. The key in this phase is to make sure the data is clean and ready for use in later stages of the process. Accurate data processing and analysis require cleansing data to remove any anomalies or errors that could skew the analysis. Next, advanced analytical techniques, often leveraging ML algorithms found in predictive analytics tools, interpret the data to identify subtle changes in equipment behavior that humans might miss. These tools process vast amounts of data quickly and with high precision to provide the insights into equipment health that drive more proactive activities.

4. Predictive Modeling

   Predictive modeling is where the data collected in the previous step is turned into the actionable maintenance decisions that drive a proactive maintenance strategy. That real-time data is combined with historical machine performance and maintenance data, then ML algorithms are applied to create and train models that can predict future equipment failures and maintenance needs. Technologies like artificial intelligence (AI), ML and statistical software play pivotal roles in developing predictive models by analyzing complex datasets to identify trends that signify potential failures. This allows maintenance teams to intervene proactively and keep their operations running efficiently. For example, in a manufacturing plant, predictive modeling might use vibration sensors installed on a conveyor belt to monitor abnormalities. By analyzing both historical and real-time vibration data over time with ML algorithms, the model can predict bearing failures before they cause a shutdown.

5. Alert Generation and Maintenance Planning

   Alert generation and maintenance planning harness the power of all that IoT data and predictive analytics to warn maintenance teams of potential issues so that they can intervene before equipment fails. Maintenance teams customarily integrate these alerts into an overall strategy to schedule repairs at the most opportune times based on the predictive insights and the organization’s operational priorities. For example, in the aviation industry, sensors on an aircraft engine might detect unusual vibrations and trigger an alert. The alert prompts maintenance planners to schedule an inspection and potential repair at the next opportunity, avoiding a more serious and costly problem that could ground the aircraft.

6. Maintenance Execution

   Maintenance execution is the culmination of all the steps listed above. Effective maintenance execution relies on a few key components: timely response to predictive alerts, skilled technicians and the right tools and parts for the job. Once a predictive alert indicates a potential problem, maintenance teams must act quickly to schedule and perform necessary repairs or adjustments. This requires well-trained personnel who understand the predictive maintenance system and can accurately interpret its recommendations.

7. Performance Analysis and Model Adjustment

   This final step underlines the importance of continuously analyzing the performance of maintenance activities in order to adjust the predictive models based on real outcomes. Doing so keeps the models accurate over time, while letting them adapt as conditions change. It involves collecting and analyzing data on the effectiveness of maintenance interventions, checking whether they’re preventing failures as predicted and identifying deviations from expected outcomes. Advanced analytics and ML algorithms can help assess data and fine-tune models for better accuracy and reliability, leading to even more reductions in maintenance costs and downtime. For example, if vibration data from a fleet of compressors indicates that predictive maintenance is creating too many interventions, a company can adjust the model’s sensitivity to better match the equipment’s actual wear and tear. As a result, resources aren’t wasted on unnecessary repairs.

When to Use Predictive Maintenance

With multiple maintenance strategies available, it’s important to know when predictive maintenance makes the most sense. Before embarking on a predictive maintenance program, several core requirements exist for success. First, companies need sufficient data to train and refine predictive models. They also need a level of technological capability to leverage the necessary technologies, such as IoT sensors, data analytics and ML, as well as the expertise to implement and manage a predictive maintenance program. Finally, it’s important to have the operational flexibility to adjust or pause equipment usage to perform maintenance determined by predictive maintenance alerts.

Assuming those conditions are met, here are the most common scenarios for using predictive maintenance.

• To protect critical equipment: Predictive maintenance is often most beneficial when companies have equipment deemed critical to operations, such as turbine generators in power plants. It can preemptively identify potential failures before they occur to keep these machines running smoothly.
• To identify failure patterns: Companies that need to constantly identify failure patterns, such as the aviation industry, can greatly benefit from predictive maintenance. For example, predictive maintenance tools can analyze aircraft engine performance data to predict component failures based on wear patterns, enabling airlines to replace parts before they lead to in-flight disruptions or cancellations.
• To reduce the cost of downtime: Companies with continuous manufacturing operations or high-demand production schedules can use predictive maintenance to prevent failures. In a power generation plant, for example, vibration analysis can detect anomalies in turbines to avoid costly unplanned shutdowns and maintain constant electricity supply.
• To reduce maintenance costs: When maintenance costs escalate, focusing resources on the equipment that truly needs attention helps avoid unnecessary maintenance tasks and extends asset life. For example, a fleet management company can analyze vehicle performance data to identify ideal maintenance intervals to lower maintenance costs while also maintaining vehicle reliability.
• To meet regulatory and safety requirements: Industries such as transportation and energy have stringent compliance needs. Predictive maintenance allows these types of companies to address equipment issues that could pose safety risks or violate regulations. For example, in transportation, oil analysis can detect engine contaminants that could lead to emissions noncompliance, avoiding regulatory penalties.

Predictive Maintenance Technologies and Methods

Maintenance teams can choose from an array of methodologies for predictive maintenance. Each of the following methods uses a mix of various technologies, ideally suited for different types of equipment.

1. Vibration Analysis

   Vibration analysis is a cornerstone of predictive maintenance that determines the condition of machines by analyzing the vibrations they emit. It measures the frequency and intensity of vibrations from machine components and compares them to established baselines for ideal performance. Identifying abnormal vibrations early means maintenance teams can schedule repairs conveniently, without disrupting production schedules. Vibration analysis typically involves specialized sensors and advanced analytics software that can detect even slight deviations from normal vibrations.

2. Motor Circuit Analysis

   Motor circuit analysis assesses the health of electric motors and their components, including windings, rotors and circuits, to identify electrical issues that may not be readily apparent. A resulting early intervention can help prevent insulation wear, power quality issues and/or imbalances in the motor windings that portend motor failure. Companies typically perform motor circuit analysis using advanced diagnostic tools that measure and analyze electrical signatures to detect anomalies. Sensors attached to various points of the motor circuit record data under normal conditions, and the data is analyzed by specialized software to identify anomalies that suggest potential issues.

3. Oil Analysis

   Many types of manufacturing equipment depend on oil: turbines, compressors, hydraulic systems, engines and pumps, for example. For these machines, oil analysis provides insights into the condition of both the oil and the machinery it lubricates. Sampling and analyzing machinery oil can detect contaminants and chemical properties that provide clues about the health and efficiency of the equipment. Failure to monitor oil can lead to issues like wear, overheating and contamination. Companies typically perform oil analysis by periodically taking samples of engine or hydraulic oils and sending the samples to labs equipped with the proper testing equipment, such as spectrometers, which look for wear metals and other contaminants; viscometers, which measure viscosity; and ferrography, which analyzes wear particles microscopically.

4. Thermography

   Also known as thermal imaging, thermography captures and analyzes thermal images of equipment to detect hot spots caused by defects in electrical systems, mechanical components or insulation failures. It’s a noninvasive method that uses infrared cameras to visualize and quantify temperature variations on the surface of machinery, indicating deeper mechanical issues like electrical faults, friction or blockages in fluid systems. Thermography scans can be performed quickly, without disrupting operational processes, which is why it’s often used to monitor critical equipment like turbines and compressors. Technicians equipped with infrared cameras scan equipment during operation to capture real-time thermal images that are then analyzed using specialized software.

5. Acoustics

   Machinery sometimes produces unusual noises, such as grinding, hissing or banging, that can indicate issues like bearing wear, misalignment, lubrication shortages or other mechanical problems that could lead to failure. Acoustic analysis, also known as sonic analysis, is another nonintrusive method usually performed while equipment is in operation. Companies typically perform acoustic monitoring on rotating machinery using specialized sensors and microphones that continuously record sound emissions from equipment during operation. The data is analyzed using advanced software that often incorporates advanced algorithms to distinguish between normal operational sounds and those that might indicate issues.

6. Ultrasonic Analysis

   Ultrasonic analysis is another form of acoustic analysis that uses high-frequency sound waves to detect changes in machinery operation that are imperceptible to the human ear. It’s more complex (and costly) than the acoustic analysis described above, which is primarily used on mechanical equipment; however, ultrasonic analysis offers a precise, nonintrusive way to detect early signs of wear or failure in mechanical, electrical and pneumatic systems. It’s especially useful for pinpointing the location of leaks in pressurized systems and for monitoring the condition of bearings and gears in rotating machinery. Ultrasound inspections are usually conducted using portable ultrasound devices. Technicians using headphones and a display unit can listen to and visualize the ultrasonic waves, and then analyze them to determine whether a problem exists and its severity.

7. Emissions Testing

   Machinery involved in combustion, such as boilers, furnaces and engines, can undergo emissions testing, which measures the gases or particles it emits. Emissions testing helps with the early detection of emissions failures, compliance with environmental regulations and fuel optimization, which improves operational efficiency and reduces costs associated with energy consumption and fines. As with other forms of predictive maintenance, sensors and analyzers are used to measure the concentration of various pollutants. The sensors are often integrated with a continuous emissions-monitoring system that provides real-time data on gases like carbon dioxide, nitrogen oxides and sulfur oxides. The data is then analyzed to assess combustion efficiency and environmental impact. Emissions testing typically involves infrared spectrometers, chemiluminescence analyzers, gas chromatographs and other specialized equipment.

8. Infrared Analysis

   The terms “infrared analysis” and “thermography” are often used interchangeably, but despite many similarities, there are slight differences. Thermography is a passive technology that uses mid- or long-wavelength infrared energy to measure heat emitted by objects. It only detects differences in heat. Infrared analysis, on the other hand, includes more active techniques that use both emitted and reflected infrared energy to analyze a system’s condition. It not only detects heat but also interprets it with sophisticated algorithms to predict potential failures more accurately. Infrared analysis encompasses a broader range of applications, including evaluating electrical components, mechanical systems and structural health, making it a more comprehensive approach to predictive maintenance.

8 Benefits of Predictive Maintenance

The main benefits of predictive maintenance — decreased downtime, reduced maintenance costs and increased equipment reliability and longevity — are well understood, have been discussed earlier in this article and are included in the list below. But there are several lesser-known benefits companies can also expect.

1. Decreased downtime: Predictive maintenance allows repairs or replacements to be scheduled during noncritical operational periods by identifying potential equipment failures before they occur. In addition, monitoring equipment conditions in real time and analyzing data trends provide actionable insights that prevent sudden breakdowns and unplanned stoppages.
2. Cost efficiency: A key goal of predictive maintenance is to avoid expensive repairs and minimize the need for emergency maintenance, which involves higher labor rates and emergency parts-shipping costs. Continuously monitoring equipment health should also reduce the frequency and severity of equipment failures.
3. Equipment longevity: When teams regularly monitor equipment to detect anomalies in equipment operation, such as increased vibration or heat, they can prevent excessive wear and tear to keep their machinery in top condition over a longer period.
4. Operational reliability: At its most basic level, predictive maintenance is about avoiding unexpected breakdowns by intervening before failures occur. This leads to smoother operations and consistent production levels.
5. Safety enhancement: Predictive maintenance enhances safety by identifying potential equipment malfunctions that could lead to dangerous failures, such as the unexpected breakdown of a pressure vessel, which can lead to explosive releases of hot steam or chemicals.
6. Production optimization: The connection between predictive maintenance and production optimization stems from the fact that preemptively identifying and addressing equipment issues helps companies avoid unexpected downtime that grinds production to a halt. Plants run at peak capacity if maintenance teams can fix problems during scheduled downtime rather than during peak production hours.
7. Strategic asset management: If companies can accurately forecast equipment failures before they occur, they can optimize maintenance schedules and extend asset life. By anticipating maintenance needs, they can plan better, allocate resources more efficiently and avoid the domino effect that often accompanies reactive maintenance strategies.
8. Inventory reduction: An important outgrowth of predictive maintenance is that it allows companies to keep only the necessary spare parts on hand rather than overstocking based on less accurate preventive models or inventory analyses. By determining the exact lifespan of machine components, companies can schedule replacements just in time and keep fewer parts in inventory, reducing the amount of capital tied up in that inventory.

Predictive Maintenance Drawbacks

The drawbacks of predictive maintenance don’t necessarily come from the technology or the process, but rather from the costs associated and expertise required to implement it well. The following list outlines common roadblocks and how to overcome them.

• Initial cost and complexity: The high cost of advanced sensors and software required to accurately forecast equipment failures can sometimes deter companies from implementing predictive maintenance. Depending on their capabilities, sensors can cost up to thousands of dollars each. In addition, analytics software can sometimes involve complex data integration and interpretation that require specialized skills. Ideally, companies should consider starting small with pilot projects to demonstrate return on investment before full-scale implementation.
• Expertise and training needs: Predictive maintenance requires targeted knowledge and training, which can be a significant hurdle for some companies. The complex data analysis and interpretation involved in predictive maintenance can make it necessary for companies to hire new staff. To manage these costs and requirements, companies generally train existing employees, collaborate with external experts or both. They also look for user-friendly predictive maintenance software that simplifies the learning curve.
• Integration and operational challenges: Integrating predictive maintenance systems with existing infrastructure can sometimes involve considerable IT challenges. In addition, daily operations can be disrupted when maintenance teams need to install and calibrate new sensors and software. One way to manage these challenges is to phase in predictive maintenance systems. Comprehensive IT team training and partnering with technology providers can ease the transition.
• Reliability and maintenance of systems: Predictive maintenance systems themselves also require regular updates and maintenance to maintain accuracy. For example, sensor drift, where sensors slowly become less accurate over time, can cause incorrect data and a potential misdiagnosis of equipment issues. Companies can mitigate these drawbacks by implementing comprehensive training programs, building dedicated teams to manage system health and choosing well-supported technologies with strong service agreements.

Predictive Maintenance Use Cases

Predictive maintenance methodologies are not one-size-fits-all; each works best with a particular type of equipment. Because different industries tend to rely on certain types of equipment, these methodologies also have specialized industry applications. Here are several examples.

• Transportation: In the railway industry, vibration analysis is used extensively to monitor the condition of rolling stock components, detecting anomalies in wheel bearings and gearboxes before they lead to failures. This not only prevents downtime but also increases passenger safety by reducing the risk of accidents.
• Healthcare: Thermography is used on complex medical equipment, such as MRI machines and CT scanners, to identify overheating in electrical components before they fail. Beyond preventing downtime and extending the life of the equipment, it also improves patient safety by reducing interruptions.
• Agriculture: Vibration analysis, motor circuit analysis and thermography are used for maintaining farm equipment. For example, farmers can use vibration analysis to detect abnormalities in tractors and harvesters. Thermography allows farmers to identify overheating components in electrical systems or machinery, preventing fires and costly repairs.
• Manufacturing: Motor circuit analysis can help detect abnormalities in the electrical systems of manufacturing equipment, such as motors and the electronics that drive them. Oil analysis can monitor the condition of lubricants in hydraulic systems, gearboxes and other equipment to identify contaminants or degradation. Companies also often use ultrasonic analysis to detect leaks or abnormalities in pressurized systems.
• Oil and gas: In the oil and gas industry, infrared and ultrasonic analyses are important for maintaining the integrity of pipelines and storage tanks. For example, infrared analysis can identify hot spots in pipelines, indicating potential leaks or insulation issues. Similarly, ultrasonic analysis can detect internal corrosion or leaks in storage tanks, which helps prevent environmental damage and maintain regulatory compliance.

Streamline and Optimize Operations With NetSuite

NetSuite offers comprehensive solutions for manufacturers that can seamlessly integrate predictive maintenance strategies into daily operations to boost equipment reliability and reduce downtime. For example, NetSuite’s advanced analytics tools analyze historical data, equipment performance metrics and environmental factors, then use ML algorithms to forecast potential issues. NetSuite includes asset-management capabilities to streamline maintenance workflows and build proactive maintenance planning. It also provides real-time visibility into equipment health, schedules maintenance tasks and automates work orders to reduce administrative overhead and enable companies to schedule maintenance at just the right time.

NetSuite’s broader suite of solutions encompasses a wide range of business operations, including finance, human resources, supply chain and customer relationship management. By integrating predictive maintenance data with other business processes, companies gain deeper insights into operations for data-driven decision-making, as well as better control of resources. All NetSuite solutions are cloud-based, offering scalability, flexibility and accessibility from anywhere, including seamless collaboration among maintenance teams and stakeholders.

As companies strive to become more competitive and efficient, the role of predictive maintenance continues to grow. It’s not just about preventing equipment failure; it’s about optimizing the maintenance process to save time and money while boosting overall productivity and performance. For businesses that depend heavily on machinery and equipment, predictive maintenance translates into fewer interruptions, better resource allocation and a clear competitive edge in operational efficiency.

Predictive Maintenance FAQs

What are predictive maintenance AI examples?

Examples of artificial intelligence (AI) in predictive maintenance include machine-learning algorithms that analyze equipment sensor data to predict equipment failures before they occur. For example, in the airline industry, AI can reduce downtime by analyzing engine performance data to forecast component failures and optimize maintenance schedules. In manufacturing, AI algorithms can detect unusual vibrations or temperature patterns in production machinery. Proactive maintenance then prevents breakdowns and boosts operational efficiency.

What is the difference between predictive and preventive maintenance?

Predictive and preventive maintenance both aim to avoid equipment failures before they happen, but they differ in their approaches. Predictive maintenance uses data analytics and monitoring techniques to predict when equipment failure is likely to occur and then performs maintenance right before the data suggests imminent failure. Preventive maintenance, on the other hand, doesn’t use analytics. Rather, maintenance is performed at regular intervals, regardless of the equipment’s condition, to prevent failures and extend the equipment’s lifespan.

What is prescriptive maintenance vs. predictive maintenance?

Prescriptive maintenance goes a step further than predictive maintenance by not only predicting when equipment failure is likely to occur but also recommending the best course of action to solve the problem, based on using advanced analytics and artificial intelligence. Like predictive maintenance, prescriptive maintenance aims to empower maintenance professionals with actionable insights to stay ahead of potential issues, ultimately improving reliability while reducing downtime and costs.