Key takeaways
To see the specific tools behind a predictive maintenance program.
Key Takeaways
Predictive maintenance (PdM) software uses data from sensors and other systems to predict equipment failures before they happen, allowing you to move beyond simple preventive maintenance.
See OEE & CMMS live in 15 minutes.
Book a demoWhile traditional PdM focuses on specialized sensor data (like vibration and thermal), the most advanced modern platforms also incorporate real-time OEE data (like slowing cycle times and micro-stops) for a more accurate diagnosis.
The best choice, Fabrico, leads this modern approach by being the only platform that seamlessly integrates a powerful PdM and CMMS engine with a native, real-time OEE data stream, turning your entire operation into an early-warning system.
This list is written and maintained by the Fabrico engineering team, and we apply the same tests to our own platform that we apply to everyone else. Fabrico appears first, and we say so openly rather than hiding it. No vendor paid for placement.
Every platform here was assessed against the same five criteria:
Where vendors publish technical documentation, we relied on it directly and avoided secondhand summaries. We revisit this evaluation as platforms ship new capabilities.
Predictive maintenance is not a single technique. It is a family of condition-based maintenance methods, and each one catches a different class of failure. Strong software platforms let you combine several techniques on the same asset and correlate the signals.
| Technique | What It Measures | Failures It Catches Early |
|---|---|---|
| Vibration analysis | Oscillation frequency and amplitude on rotating equipment | Bearing wear, imbalance, misalignment, looseness |
| Infrared thermography | Surface temperature patterns | Overheating electrical connections, friction hot spots, insulation faults |
| Oil analysis | Wear particles, viscosity, and contamination in lubricants | Gearbox wear, lubricant degradation, seal failure |
| Ultrasonic monitoring | High frequency sound beyond human hearing | Compressed air leaks, early stage bearing faults, electrical arcing |
| Machine vision | Camera images of components and product flow | Visible wear, belt and conveyor drift, leaks and smoke |
For rotating equipment, vibration usually gives the earliest warning: most bearing failure modes produce measurable symptoms well before functional failure. Fixed electrical assets are better served by thermography, while gearboxes and hydraulic systems reward regular oil analysis.
Predictive maintenance uses real condition data, such as vibration, temperature, current draw, and oil condition, to estimate when a specific asset will fail, so work is scheduled just before it is needed. It differs from preventive maintenance, which services equipment on a fixed calendar regardless of actual condition.
The main benefits are fewer unplanned stops, longer mean time between failures, lower spare parts spend because components run closer to their full useful life, and safer work because interventions are planned instead of reactive. That stability also lifts availability, typically the biggest lever in a plant's OEE.
The core techniques are vibration analysis, infrared thermography, oil analysis, ultrasonic monitoring, and machine vision, often supplemented by motor current analysis. Each targets different failure modes, and mature programs layer two or three techniques on their most critical assets.
A CMMS organizes maintenance work: assets, work orders, spare parts, and history. Predictive maintenance software decides when that work should happen by analyzing condition data. The two categories are converging, and the strongest results come from platforms where predictive alerts flow straight into planned work orders.
A true PdM platform is more than just a sensor. It's a complete system for turning data into action.
Flexible Data Integration: Can it connect to a wide range of data sources, from specialized vibration and thermal sensors to your real-time production data?
AI-Powered Anomaly Detection: Does it use machine learning to identify complex patterns and provide simple, actionable alerts, not just raw data streams?
Integrated "Cure" Workflow: Can a predictive alert automatically generate a planned work order in the CMMS to ensure the issue is addressed?
Usability: Is the system simple enough for a maintenance manager to use effectively, or does it require a dedicated team of data scientists to interpret the results?
Here is our breakdown of the top platforms that are leading the charge into the future of maintenance.
The Verdict: Fabrico is our top choice because it represents the future of practical, accessible predictive maintenance. It is the only platform built from the ground up to use real-time OEE data as a primary input for its predictive engine, creating a more holistic and powerful system than tools that rely on sensor data alone.
Why it Wins (The Differentiator): While other platforms are excellent at predicting a failure based on a vibration alert, Fabrico can predict a failure because its OEE module has detected that a machine's cycle time is slowly degrading and its rate of micro-stops is increasing. This operational data is a powerful, and often overlooked, leading indicator of failure.
By combining this real-time operational diagnosis with a powerful CMMS cure, Fabrico turns your entire production line into a predictive early-warning system.
Key PdM Features:
Connects to traditional condition-monitoring sensors (vibration, thermal, etc.).
Uses native, real-time OEE data (cycle time, throughput, micro-stops) as a core predictive input.
AI-powered anomaly detection that provides simple, actionable alerts.
Automated work order generation from predictive alerts, allowing you to plan the fix before the failure.
Best Suited For: Modern manufacturers who want a practical, user-friendly way to implement a truly data-driven predictive maintenance strategy that leverages the rich operational data they already have.
Description: As part of the Fluke family, a global leader in measurement tools, eMaint is a powerhouse for sensor-based predictive maintenance. Its key strength is its deep, seamless integration with Fluke's wide range of world-class condition-monitoring sensors, making it a top choice for companies investing heavily in a sensor-first reliability strategy.
Description: IBM Maximo is a massive EAM platform with powerful predictive capabilities. It is designed for large, asset-intensive enterprises (like in the energy or transportation sectors) that need to incorporate predictive analytics into a broader corporate strategy of managing the entire financial and operational lifecycle of their assets.
Description: Fiix is a robust cloud CMMS with a growing set of predictive maintenance features. Its strength within the Rockwell Automation ecosystem lies in its ability to connect to and pull data from a wide variety of factory floor PLCs and control systems, using that operational technology data to inform its predictive models.
Description: Augury is a more specialized platform that focuses almost exclusively on AI-powered machine health and predictive maintenance. It combines its own proprietary sensors with an advanced AI engine to provide highly accurate failure predictions and detailed diagnostic insights, often backed by a team of human experts.
See OEE & CMMS live in 15 minutes.
Book a demoWhat is the difference between Preventive and Predictive Maintenance?
Preventive Maintenance (PM) is performed on a fixed schedule (time or usage) to prevent failures. Predictive Maintenance (PdM) uses real-time data and AI to estimate when a failure is likely to occur, so work can be scheduled before it happens, allowing you to perform maintenance only when it is truly needed.
Do we need a lot of historical data to start with PdM?
Yes, the more data the better. An effective AI model typically needs several months of clean performance data and maintenance records to learn your equipment's unique failure patterns and begin making accurate predictions.
Can we start with just one or two critical assets?
Absolutely. The best way to start a PdM program is to run a pilot on one or two of your most critical and failure-prone assets. This allows you to prove the ROI and build a business case before scaling to the rest of the plant.
The best predictive maintenance software doesn't just look at one stream of data; it looks at the whole picture.
It combines the deep insights of condition monitoring with the real-world reality of your production performance to give you a true, actionable glimpse into the future of your factory.
Ready to see how a modern, integrated platform can turn your OEE data into a powerful predictive tool?
Book a personalized demo of Fabrico today.
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