You might be familiar with this scenario if you work in industrial maintenance: it’s 2 am, and your phone rings. A critical motor has seized, and suddenly, your carefully coordinated schedule for the week is out the window. This situation isn’t just bad luck. It’s the daily reality of a strategy built on reactive fixes or time-based calendars that can’t actually see what’s happening inside your equipment.
If you’re looking for a way to catch equipment issues earlier and focus your resources where they matter most, there’s a better approach: one that puts real-time machine data at the center of your program. Condition-based maintenance (CBM) allows you to stop chasing failures and start planning work on your own terms.
In this guide, we cover how maintenance based on condition works, how it differs from other approaches, and how sites like Coca-Cola Consolidated now use CBM to prevent failures and keep production on schedule. Whether you’re new to the concept or looking to optimize an existing program, you’ll find a practical roadmap for implementing a condition-based maintenance system that your floor team will actually trust.
Key highlights:
- Condition‑based maintenance (CBM) uses real‑time asset monitoring to assess equipment condition and support better work planning.
- Transitioning from time-based to condition-based maintenance helps reduce waste, unplanned downtime, and overall repair costs.
- The CBM lifecycle involves capturing high-frequency equipment performance data through IoT sensors, using AI to detect subtle anomalies, and scheduling repairs during planned windows.
- Augury’s Machine Health Solutions combine CBM with predictive and prescriptive AI capabilities that help you increase uptime and build more reliable operations.
What is condition-based maintenance (CBM)?
Condition-based maintenance (CBM) is a strategy that relies on real-time asset condition monitoring, using methods such as vibration and magnetic flux analysis, to determine when you need to perform maintenance work.
The CBM process starts by defining what normal looks like for your machines (e.g., typical operating temperatures). With these parameters in place, IoT sensors monitor equipment performance in real time, detecting changes such as increased heat or new vibration patterns. When a machine crosses a set threshold, you get an alert so you can take action.
What are the advantages of condition-based maintenance for industries?
Condition-based maintenance helps you gain clearer insight into how your assets are performing so you can plan your work more efficiently. According to a Forrester Total Economic Impact™ study*, the composite organization reduced maintenance costs by 15% per asset by eliminating unnecessary work and emergency repairs.
Advantages of condition-based maintenance include:
- Reduced unplanned downtime: By catching issues like bearing wear or misalignment early, you can maximize machine uptime and keep your production targets on track.
- Lowered maintenance and repair costs: Emergency parts and rush shipping drive up expenses. By fixing equipment problems before they escalate, you avoid unnecessary spending and focus your maintenance activities where they matter most.
- Minimized product waste: When machines slip out of spec, scrap and waste add up. Early detection lets you catch performance dips before they impact quality, so you keep more good product moving down the line.
- Extended asset life: Tackling small issues like excess heat or vibration early helps prevent lasting damage and keeps your machines running longer.
- Enhanced safety and risk mitigation: Tracking asset condition with Machine Health data lets you plan repairs during scheduled windows, reducing the unplanned, high-pressure situations where your maintenance and reliability teams face the greatest risk.
- Improved team morale with less emergency work: No one wants to stay late on a Friday due to a compromised belt. When your workday is more predictable, you reduce burnout and give your team time to focus on real wins rather than constant repairs.
See why condition-based maintenance matters even more when everything else is unstable with our complete guide.
Maintenance based on condition vs. other methods
Maintenance based on condition helps you make decisions driven by actual equipment health rather than fixed time intervals or simply reacting to breakdowns. But most plants aren’t there yet. According to the Machine Health Is Business Health report, 74% of respondents still rely on traditional preventive maintenance practices: some scheduled, but mostly reactive, manual maintenance.
Here’s how CBM differs from other common maintenance approaches:
Condition-based maintenance vs. preventive maintenance
Preventive maintenance relies on fixed intervals, like changing a part every six months, regardless of its condition. This strategy often leads to over-maintenance, where you replace perfectly good components. Condition-based maintenance, on the other hand, uses real-time signals to trigger work. You step in only when the data shows clear signs of wear, allowing you to focus your team’s efforts on the machines that actually need attention.
Condition-based maintenance vs. predictive maintenance
The difference between condition-based maintenance and predictive maintenance comes down to the timeline. CBM identifies a problem that has already started; you are addressing a detected symptom, such as a worn bearing or a loose belt. Predictive maintenance (PdM) uses that same data to forecast when a failure is likely to happen in the future. By combining these two approaches, you get the ability to see a fault the moment it starts and the foresight to plan the repair weeks in advance.
See also: Prescriptive vs predictive vs preventive maintenance
Types of condition-based maintenance techniques
Relying on a single signal won’t give you the full story on your equipment’s health. Using different types of condition-based maintenance techniques helps you get a more complete view of your assets and catch developing faults before they become failures.
| Types of condition-based maintenance techniques | Description | Importance |
|---|---|---|
| Vibration sensing | Vibration monitoring systems capture the mechanical oscillations of rotating components to detect faults like imbalance, misalignment, or bearing wear. | High-resolution vibration data is your early warning sign of faults, catching mechanical wear weeks before a failure disrupts your production. |
| Temperature monitoring | Temperature sensors track surface and ambient heat trends to detect early friction, lubrication issues, or cooling system faults in bearings, motors, and other components. | Spotting gradual thermal anomalies before they escalate to overheating allows you to intervene before a machine fails or begins affecting product quality. |
| Magnetic flux analysis | Magnetic flux monitoring techniques track the electromagnetic field around a motor to calculate actual speed and identify internal electrical faults (e.g., rotor issues). | Magnetic flux data provides the context needed to verify vibration spikes, helping you accurately diagnose resonance, detect early electrical problems, and minimize false alerts. |
| Ultrasound detection | Ultrasound sensors capture high-frequency sound waves produced by air leaks, electrical arcing, or early-stage friction in ultra-low-RPM machines. | Ultrasound identifies the earliest signs of friction and leaks, giving you the most possible lead time to plan your maintenance strategy and resources. |
CBM in action: A condition-based maintenance example to inspire you
Now, let’s look at a condition-based maintenance example. Consider gearboxes: you know they are critical to your facility. If you’re following a traditional maintenance schedule, you’re likely taking them offline every 3-6 months, or per the manual’s recommended service interval, just to check the oil and inspect for wear. With CBM, the asset’s actual performance guides your decisions. By monitoring real-time signals from the gearbox, you can see when it is running smoothly and safely extend its service interval.
The impact on the plant floor is clear. At Coca-Cola Consolidated, the largest Coca-Cola bottler in the US, the Charlotte, NC plant runs nonstop across six bottling lines. Missing a single fault can bring everything to a halt. Before moving to condition-based maintenance, their team depended on manual rounds and fixed schedules, which often missed issues developing between checks.
By adopting a proactive maintenance approach with Augury’s Machine Health Solutions, the Coca-Cola team identified an issue with a critical filler gearbox before it could have caused a major shutdown. They scheduled the repair during a planned window and kept production on track. With an 87.5% alert response rate, their team shows that when you have reliable diagnostics, you can move from constant firefighting to confident planning.
Understanding the CBM lifecycle
The CBM lifecycle is straightforward: you capture signals, analyze the data, plan the fix, and carry out the repair. This is how the process works step-by-step:
1. Condition-monitoring sensors capture machine data
CBM begins on the plant floor, where IoT sensors installed on your critical assets continuously monitor physical signatures like vibration, temperature, and magnetic flux. These devices capture high-frequency performance data while your machines are running, providing a constant digital record of their current state. Because these sensors transmit information wirelessly to a cloud-based platform, you no longer have to rely on manual inspections or point-in-time checks.
2. CBM systems detect issues through data analysis
Automated condition-based maintenance systems analyze real-time data against predefined thresholds or baselines to identify deviations, like elevated vibration, that signal degradation. Modern solutions like Augury’s Machine Health employ advanced AI algorithms to scan for subtle anomalies, accelerating issue detection beyond traditional rule-based methods.
3. CBM insights enable teams to plan equipment management
CBM insights let the machine’s actual condition guide your schedule, so you can move away from time or route-based checklists. You finally have the data to justify every maintenance task to leadership, ensuring you focus your resources on the assets that need the most attention.
What are the challenges of adopting maintenance based on condition?
Shifting to maintenance based on condition is a big step, and without the right support to guide your team through both technical and cultural changes, progress can stall before you see a return. By tackling these common hurdles early, you set up your strategy for success:
- Fragmented data silos: Disconnected maintenance systems make it difficult to scale your wins. Building a strong industrial data foundation is your first step toward long-term success.
- Alert overload: Generic alarms often lead to alert fatigue, which can cause your team to miss a critical asset failure. Using a condition-based maintenance system that provides Guaranteed Diagnostics™ helps you cut through the noise, giving you the specific insights you need to act with total confidence.
- Technician adoption: Techs will trust a new monitoring solution when they see that the data helps protect their time. When a system consistently shows it can tell the difference between a healthy machine and a real fault, it builds confidence on the floor and helps create a more predictable, less stressful workday.
Learn why everyone’s ready for a predictive maintenance program.
Best practices for implementing a condition-based maintenance system
To get the most from a condition-based maintenance system, you need a partner who can help you bring insights into your daily workflows and build trust with your technicians. Consider these five best practices for implementation:
- Identify your specific CBM use case: Zero in on your facility’s challenges and define goals for your condition-based maintenance program. Maybe it’s cutting unscheduled downtime on line 5 fillers, or reducing urgent fixes on a crucial motor. When you focus on the equipment with the greatest impact, it’s easier to demonstrate the value of CBM and build momentum to roll it out site-wide.
- Choose a complete solution: Opt for a CBM provider who handles the full journey, from hardware installation to ongoing support from industry pros who speak your language.
- Look for plant-floor-level insights: Machine health data is most powerful when your maintenance team has direct access to it. Prioritize platforms with dashboards that deliver the right information to everyone who needs it, from operators to managers.
- Select a champion to drive plant-wide adoption: Every plant has that go-to person. Someone who your team trusts and looks up to. Bring them into the CBM project early. Their buy-in will spark a culture shift and help the new mindset catch on.
- Track and share your maintenance wins: As your CBM rollout takes shape, measure its success against the goals you defined early on. Once you’ve built a track record, you’ll be ready to set even bigger targets for your program.
Putting these best practices into action is especially important when you’re dealing with complex machines, where physical constraints make traditional monitoring difficult. A leading pet food manufacturer, for example, partnered with Augury to gain visibility into a critical extruder. Even with limited sensor placement due to the machine’s configuration, our AI detected subtle changes in vibration indicative of mechanical looseness and misalignment.
The onsite team acted on the early AI alerts and worked with Augury’s vibration experts to create work orders and replace the motor before it failed. This proactive step avoided eight hours of unplanned downtime and saved more than $100,000 in replacement costs.
Dive deeper: 9 tips on getting started with Machine Health
Modernize your maintenance program with Augury’s Machine Health Solutions
Augury’s Machine Health Solutions provide the real-time visibility of a condition-based maintenance system, while layering on both predictive and prescriptive capabilities. With our AI-driven insights, you know when a failure is likely to occur and get step-by-step instructions to fix it, with the help of certified CAT III and IV vibration analysts.
Augury’s comprehensive asset coverage lets you apply this data-driven approach across your entire facility, from everyday equipment to your most complex assets, ensuring nothing gets overlooked.
Schedule a demo to see how Augury’s solutions can help you cut costs, modernize your maintenance program, and build more reliable operations.
Frequently asked questions
What is the difference between time-based maintenance (TBM) and condition-based maintenance (CBM)?
Time-based maintenance (TBM) relies on fixed intervals or the calendar to trigger work, regardless of how the machine is actually performing. Condition-based maintenance (CBM) uses real-time data to trigger repairs only when an asset shows actual signs of wear or distress.
Take a critical gearbox, for example. With TBM, you might swap out the lubricant every six months, even if the oil is still in great shape. CBM lets you use oil analysis to check for viscosity, water, or wear metals. That way, you can change the oil when it actually needs it, helping your facility reduce maintenance costs.
What is the best condition-based maintenance software?
The best condition-based maintenance software is a solution that delivers reliable, actionable insights to your team. Look for technology that brings together high-resolution sensing, prescriptive AI, and real human expertise. Here’s what to expect:
- Prescriptive diagnostics: Clear guidance that tells your team exactly what’s wrong and how to fix it
- Human-in-the-loop expertise: Certified vibration analysts who review AI findings, so your team can trust the results and avoid wasted effort
- Ease of use: An intuitive interface that your team can pick up quickly
- Integration capabilities: Seamless connection with your existing CMMS, so insights become work orders without delay
Augury’s Machine Health Solutions are built to be that comprehensive partner. We go beyond traditional condition-based maintenance software by combining prescriptive AI with human-in-the-loop guidance to deliver Guaranteed Diagnostics™ that protect every critical asset across your facility.
Which industrial condition-monitoring platforms support multi-asset tracking?
Industrial condition-monitoring platforms like Augury help you track the health of all your machines, from critical assets to supporting equipment. With our Machine Health Solutions, you get a unified view of your facility, so your team can spot issues early and act before failures happen.
Augury supports a wide range of industrial assets, including:
- Critical rotating machines like pumps, motors, and fans
- Slow-moving equipment such as kilns, large gearboxes, and rotary drums
- Assets in hazardous zones where explosive or flammable atmospheres require specialized, safety-certified sensors
- Medium-criticality machines that represent the essential balance-of-plant fleet
See the questions to ask when evaluating an AI-powered condition monitoring solution.
Can you overmaintain assets?
Yes, especially in facilities that stick to rigid preventive maintenance checklists, you risk overmaintaining assets more than you might think. Every time you open up a healthy machine just because the calendar says so, you risk causing new problems like misalignment, seal damage, or contamination.
Condition-based maintenance helps you avoid these self-inflicted issues. Instead of relying on the calendar, you let real machine performance guide your decisions. That way, you only take equipment offline when there’s a clear reason, which optimizes asset care and keeps your team focused on the repairs that actually protect uptime.
*A commissioned study conducted by Forrester Consulting on behalf of Augury, July 2025.
