For any experienced maintenance leader, the goal is always to move beyond a reactive firefighting model. While a solid preventive maintenance program is a crucial first step, a truly optimized strategy requires getting ahead of failures with even greater precision. This is the promise of predictive maintenance (PdM): using real-time data to fix assets at the exact right moment—just before they fail.
This guide serves as a practical review of the best predictive maintenance tools and platforms heading into 2026. We’ll break down the top solutions on the market and explain the key features you need to effectively implement a data-driven maintenance program that maximizes uptime and improves operational efficiency.
How Does PdM Work?
A successful predictive maintenance (PdM) program is a closed-loop system that transforms raw equipment data into actionable maintenance tasks. The process can be broken down into three key stages: data collection, data transmission, and data analysis.
1. Data collection (Condition-monitoring sensors)
The foundation of any predictive maintenance program is the use of condition-monitoring sensors to evaluate assets’ performance and overall equipment health. This involves installing sensors on your critical equipment to capture data and facilitate the evaluation of the asset’s efficiency.
Sensors can capture different aspects such as temperature and pressure. Common types of sensors used in PdM include:
- Vibration analysis sensors to detect imbalances or bearing wear in rotating equipment.
- Thermal imaging sensors to identify overheating in electrical components.
- Acoustic analysis sensors to recognize changes in sound patterns that indicate a potential fault.
- Oil analysis sensors to detect contaminants or degradation in lubricants.
With condition-monitoring sensors, maintenance teams can have information about the asset’s operating condition without having to open the machine. This diagnosis automation prevents teams from having excessive unplanned downtime.
2. Data transmission (IoT network)
Once the data is collected, it needs to be sent to a central system for analysis. This is accomplished using the Internet of Things (IoT) technology. The sensors are connected to a network (which can be Wi-Fi, cellular, or a wired LAN) that transmits their real-time data to a central database. This database is typically part of a modern, cloud-based CMMS platform.
3. Data analysis (The predictive algorithm)
This is where the ‘predictive’ part happens. The massive amount of incoming sensor data is fed into a predictive algorithm. This software uses artificial intelligence and machine learning to analyze the data in real-time. It compares the asset’s current performance against its established normal operating baseline to detect subtle anomalies and patterns that are invisible to the human eye.
When the algorithm detects a deviation that indicates a probable future failure, it automatically triggers an alert or generates a work order in the CMMS. Through this exchange of information, maintenance managers can see all physical assets as a whole, allowing them to make sense of what’s happening in the machines and identify any areas that require attention.
The Best Types of Predictive Maintenance Tools
Optimal returns on the maintenance strategy require selecting the best combination of tools and technology for the job.
Let’s explore the possibilities:
Infrared Analysis Sensors
Sensor data is vital in a predictive maintenance program. IoT Sensors measure different aspects of machines, including temperature, pressure, sounds, and more.
Infrared sensors are used specifically to compare the difference in temperature between components in one view or multiple views over time. Through Infrared Radiation (IR), sensors identify existing or upcoming issues with various types of assets, components, and materials.
Through the varying levels of light of a radiation’s wavelength, which is invisible to human eyes, the sensor identifies an item’s temperature. IR can compare temperature differences between components in one view or multiple views.
What is it used for?
Infrared analysis can be used to monitor bearing temperatures in large motors, measure piping conditions, calculate process temperatures, estimate plumbing conditions, verify solar panel conditions, evaluate electrical components conditions (prevalent for ARC flash analysis), check temperature variations of mechanical components (such as motor cases), verify insulation situation, and more.
Is it worth it?
Infrared thermography cameras and sensors are a common investment for managers structuring a PdM program. Thermal imaging is a great way to quickly measure and compare heat signatures that are not visible to naked eyes, without causing operational disruptions or putting technicians at risk.
It also allows workers to locate abnormal conditions obscured by other parts of the equipment. But keep in mind that if you need to measure temperature against reflective surfaces, that’s not the best option.
Motor Circuit Analyzers
A motor circuit analyzer is a predictive maintenance solution that helps get a complete picture of an equipment’s motor system’s electrical health. Motor circuit analyzers can find faults and potential equipment failures based on electric motors’ components.
Motor circuit analysis leverages electric signature analysis (ESA) to identify these faults. ESA measures a motor’s supply voltage and operating current to determine issues. ESA works on AC and DC motors.
What is it used for?
Motor circuit analysis is used to identify problems in incoming power and in motor mechanical components. It detects issues in the stator winding, abnormalities in the bearing, rotor problems, issues in the coupling, problems in attached load, efficiency and system load, and others.
Is it worth it?
Motor circuit analysis is a practical way to verify the conditions of different pieces of equipment while they are still running. Most motor circuit analyzer tools allow tests to be conducted in about 2 minutes.
It is used only to check electrical components — connected components can be tested, but not the entire equipment chain.
Vibration Analysis Sensors
Vibration analysis sensors read parts vibration to detect problem signals and send over information to a database. When connected to a modern CMMS, it is possible to perceive changes over time by comparing current to previous data. CMMS machine learning also organized data into actionable information.
What is it used for?
Vibration is one of the top indicators for impeding failures when it comes to rotating equipment. Increased vibration intensity indicates equipment wear off, which, without timely intervention, can lead to major breakdowns.
Vibration analysis is used to detect faults related to misalignment, mechanical looseness, gear defects, lack of lubrication, resonance, rubbing, cavitation, corrosion, and more. Therefore, it can be used in a variety of equipment types across all industries.
Is it worth it?
One of the advantages of vibration analysis is that it can be used in a variety of assets. It collects real-time data about an equipment’s velocity (speed of the vibration), displacement, and vibration frequency. Within one month of having sensors collecting data, there is enough information to take action. Keep in mind that optimal results require knowledge to select the proper tools.
Ultrasonic Analysis Microphone
Ultrasonic analysis (UA) detects possible issues in machines by using a sensitive microphone to pick up high-frequency sounds. The sound waves are sent to CMMS software and turned into audio and digital data.
Just like with vibration analysis, computer software compares new UA data with known previous recordings for performance tracking.
Portable UA sensors collect data both for immediate use or to compose a complete data set for further analysis.
What is it used for?
Ultrasound analysis can be leveraged for electrical inspection, steam trap maintenance, optimal lubrication practices, leak detection, mechanical inspection, electric arc flash detection, and valve testing.
Some UA tools have embedded thermometers or cameras that allow even higher data analysis.
Is it worth it?
Ultrasound analysis has a lower acquisition cost when compared to other predictive maintenance technologies. It is also easy to use and to understand since the sounds can often be heard by technicians when using the software.
Another benefit of leveraging UA is that it offers rapid detection and precise location of potential issues. From there, technicians often have the ability to take measurements at a distance without contact.
One of the things to note is that repeatability is hard due to the position of the detector from a test subject.
Laser-shaft Alignment Tool
Laser-shaft alignment tools are used to test precision-align rotating shafts in a facility. That is because poor installation practices when putting equipment into service are one of the most frequent causes of mechanical failure.
What is it used for?
Misaligned components, both angular or offset misalignments, place extraordinary pressure on all elements within an asset’s drive train. Bearings frequently bear the brunt of misaligned shafts.
Laser-shaft alignment tools feature single laser measurement technology to decrease inaccuracy from backlash.
Is it worth it?
Laser-shaft alignment tools provide an intuitive way to complete machine alignments. It guarantees proper shaft alignment, dramatically reducing mechanical failures.
One of the downsides is that equipment may need to be stopped to conduct analysis.
Leverage the Best Combination of Modern Software
Since predictive maintenance involves more moving parts than any other maintenance approach, it is worth it to make an educated choice when investing in the tools.
The tools mentioned on this list are some of the most commonly used in predictive maintenance activities. They can be leveraged by organizations in many different industries.
Note that there are other PdM tools available for specific industries that are crucial to compose an efficient maintenance schedule and help reduce reactive maintenance tasks.
Oil analysis tools, for example, are indispensable for oil and gas organizations.
However, regardless of the industry that your organization operates in, investing in CMMS software, such as Limble, is key in every PdM strategy. That’s because it integrates all sensor data and organizes it into valuable predictive analytics.
Predictive maintenance technology is in constant evolution, so being on top of the best tools is crucial to building a strong and cost-effective maintenance strategy.
Limble for Predictive Maintenance
A successful predictive maintenance program depends on having a powerful yet user-friendly hub to manage the entire process. Limble is designed from the ground up to be that central system.
Limble seamlessly integrates with your IoT sensors to collect and analyze real-time condition data. When our predictive algorithms detect an anomaly that points to a potential failure, the system automatically generates a detailed work order and assigns it to the right technician. This transforms your PdM strategy from a complex data analysis project into a simple, automated workflow that prevents downtime.
If you’re ready to see how a modern CMMS can serve as the hub for your entire PdM program, request a demo to see Limble’s predictive maintenance capabilities in action.
FAQs
What’s the difference between a CMMS with PdM and a full EAM system?
A CMMS with PdM capabilities is focused on using real-time data to optimize maintenance work and improve equipment health. An EAM (Enterprise Asset Management) system has a broader scope, managing the entire asset lifecycle from a financial perspective, including procurement and disposal.
While an EAM may have PdM features, a dedicated predictive maintenance software is often more specialized for the maintenance team‘s needs.
How is a digital twin used in predictive maintenance?
A digital twin is a virtual, real-time model of a physical asset. In predictive maintenance, sensor data from the physical asset is fed into its digital twin. This allows teams to run simulations, test failure scenarios, and use AI-driven forecasting to predict equipment issues with even greater accuracy, all without affecting the real-world equipment.
How do I choose between different predictive maintenance software providers?
When evaluating providers, look beyond just features. Consider their industry experience by reviewing customer case studies. Ensure their software can integrate with your existing systems and that they offer robust training and support.
The best solution will not only have powerful data analytics but will also be easy for your team to use.
What is the role of robotics in predictive maintenance?
The use of robotic technology is a growing trend in PdM. Drones equipped with thermal cameras can inspect hard-to-reach areas, and autonomous robots can perform routine sensor checks. As this technology matures, AI-powered robots will increasingly be used to not only collect data but also to perform the maintenance work that the PdM system identifies.