What is Condition-Based and Predictive Maintenance

 A couple definitions

What Is Predictive Maintenance?

Predictive Maintenance is a data-driven approach focused on forecasting asset failures. The goal is simple: intervene just before the point of functional failure, not too early and never too late.

PdM relies heavily on the collection and analysis of real-time or near-real-time condition data using specialized technologies.

Predictive Maintenance is analytical and often predictive in the statistical sense: we’re using pattern recognition, trend analysis, and even machine learning to predict the remaining useful life of an asset or component.

In practice, PdM programs are more mature and strategic. They require structured data governance, skilled analysts, and integration with an Asset Performance Management (APM) platform.

What is CBM

Condition-Based Maintenance (CBM) involves continuously or periodically monitoring the actual condition of assets to determine the necessity of maintenance. Maintenance activities are triggered only when specific condition thresholds are exceeded, indicating potential wear or degradation.

This approach ensures that maintenance actions are aligned with the health of the equipment, avoiding unnecessary interventions while addressing issues before failures occur.

CBM is inherently proactive, emphasizing the detection and resolution of potential problems before they escalate. It focuses on asset health by continuously assessing operational parameters, such as temperature, pressure, vibration, and more.

The Fine Line Between CBM and PdM

Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM) are both cornerstones of proactive maintenance.

CBM focuses on real-time monitoring and reacting to condition thresholds, ensuring timely action based on asset health. PdM, on the other hand, takes a predictive approach, leveraging advanced analytics to foresee failures and schedule maintenance with precision.

The key distinction between PdM and CBM lies in the depth and sophistication of the analysis, and what decisions are made with that information.

• Condition-Based Maintenance responds to data that indicates a deviation from normal operating parameters. For example, if a vibration sensor crosses a warning threshold, an alert is triggered. CBM is event-driven and typically uses setpoint-based alerts to indicate when intervention is required. It tells you:
• Predictive Maintenance, in contrast, uses historical data, trend analysis, and predictive algorithms, often powered by machine learning or statistical models, to forecast when a failure is likely to occur. PdM goes beyond detection to deliver prognostics, estimating how much time is left before failure.

Think of it this way: CBM reacts to what the machine is telling you right now. PdM listens to the same signals but interprets them through a lens of future risk. Both depend on the same monitoring infrastructure, but PdM applies deeper intelligence and modeling to optimize the timing of intervention.

In practice, many organizations implement both in tandem, starting with CBM as the foundational layer, then evolving toward PdM as data maturity and technology adoption progress and that is why PdM and CBM are often designated as the same strategy.

How Do Predictive Maintenance (PdM) and Condition-Based Maintenance (CBM) Work?

Predictive Maintenance (PdM) and Condition-Based Maintenance (CBM) represent two approaches to reducing unplanned downtime and extending asset life.

Both rely on insights from condition monitoring technologies into equipment health, but they differ in how they interpret this data and what actions they trigger.

To understand how they work, it’s essential to first look at the foundation they share: condition monitoring technologies.

The Role of Condition Monitoring Technologies

At the core of both PdM and CBM are non-invasive condition monitoring tools that gather operational data from physical assets. These technologies include analyzer, sensors and diagnostic tools designed to measure parameters such as:

Vibration
Temperature
Lubricant quality
Electrical current
Pressure
Ultrasound and acoustic emissions

Condition monitoring enables maintenance teams to assess asset health without shutting down equipment or opening components.

What They Have in Common

PdM and CBM both start with this same foundation: condition monitoring data captured from machines in operation. This data is used to assess whether assets are operating normally or showing early signs of degradation.

By focusing on actual equipment condition rather than scheduled intervals, both strategies enable maintenance teams to avoid unnecessary work, reduce costs, and improve uptime.

The P-F Curve: Timing the Intervention

To truly understand the difference between CBM and PdM, it’s helpful to visualize the P-F Curve, a model that describes the degradation of an asset over time.

• The P-point (Potential Failure) is when a defect or fault becomes detectable by condition monitoring.
• The F-point (Functional Failure) is when the asset can no longer perform its intended function.

The time between P and F, known as the P-F interval, is the window during which maintenance teams can act to prevent failure.

• CBM responds once a condition crosses a warning threshold, typically after the P-point has been reached.
• PdM, however, aims to detect patterns leading up to the P-point, forecasting failure before conventional thresholds are crossed and providing a longer planning horizon.

Route-Based vs. Sensor-Based Approaches 

Condition-based maintenance and Predictive maintenance strategies should be tailored to the criticality of each asset. Some equipment may only require periodic inspections, such as every 30 days, while others demand constant monitoring to avoid significant risks. 

And that’s why there are multiple approaches to condition monitoring:

By aligning the approach with asset criticality and leveraging the right technologies, predictive maintenance ensures resources are effectively allocated while maintaining operational reliability. 

As per the maintenance strategies, route-based and sensor-based strategies work hand in hand, creating a hybrid approach that adapts to specific needs. The decision to combine or rely on one approach comes down to the criticality of the equipment, operational goals, and available resources. 

3 Reasons to Invest in CBM / PdM

Minimize Planned and Unplanned Downtime

Every hour of equipment downtime comes with a cost and predictive maintenance helps avoid both sudden breakdowns and unnecessary planned shutdowns by catching issues early.

CBM/PdM technologies allow maintenance teams to monitor equipment health without stopping production. This non-intrusive monitoring reduces the need to open up or stop machines to analyze its condition.

Maintenance Costs Reduction

Traditional maintenance programs can lead to over-maintenance, replacing components or performing inspections that may not be needed. Predictive and condition-based maintenance flips that model: maintenance happens only when the data says it’s needed.

This leads to:

• Lower labor costs (less overtime and emergency work)
• Fewer urgent part orders
• Less wasted maintenance effort on perfectly healthy assets

Over time, this translates into significant budget optimization, especially when managing a large number of assets.

Free Up Your Maintenance Team & Shift on the PF Curve

In traditional reactive environments, maintenance teams spend their days “putting out fires.” But with PdM and CBM, technicians are alerted earlier on the P-F Curve.

This gives teams a valuable window of time to act strategically and:

• Plan work orders
• Group tasks efficiently
• Perform root cause analysis
• Conduct quality inspections instead of rush jobs

Challenges of Predictive Maintenance

Condition-based maintenance and Predictive maintenance, while powerful, comes with their own set of complexities: 

• High Initial Investment: Implementing a predictive maintenance program requires a significant outlay, particularly when it comes to the specialized sensors and software needed for data collection.  
• Need for Technical Expertise: It’s not just about having the right tools , you need the right people too. CBM & PdM relies heavily on advanced technologies, so training and possibly hiring skilled technicians is a must. The expertise required comes with its own price tag, as teams must learn to interpret and act on complex data. 
• Complex Data Management: Predictive maintenance systems produce a massive volume of data, from vibration metrics to thermal imaging. To turn this data into actionable insights, it’s crucial to centralize it within a unified platform. Without proper infrastructure and tools like an APM, which streamline and organize this information, businesses risk being overwhelmed by fragmented data that hinders rather than helps decision-making. 

Implementing a Predictive Maintenance Program

Rolling out a predictive or condition-based maintenance strategy involves several key steps to ensure success: 

• Asset Criticality Ranking: The first thing you need to do is identify the critical equipment in your operation. These are the pieces that, if they fail, could cause serious disruptions or safety risks. By narrowing your focus, you can direct resources where they matter most, boosting your ROI. 
• Technologies for the Right Assets: Once you’ve pinpointed your critical equipment, it’s time to pick the right technologies for the job. Different machines require different types of monitoring – for example, vibration sensors for rotating machinery, temperature sensors for climate control systems. You also need software that can keep pace with the data, analyzing it and delivering actionable alerts to guide your team’s response. 
• Training & Implementation: Even the best technology is only as good as the people using it. Investing in thorough training is vital to make sure your operators, technicians, and engineers understand how to use the tools, interpret the data, and respond appropriately to any anomalies. 
• Ongoing Monitoring and Adjustments: Predictive maintenance isn’t a “set it and forget it” deal. It requires continuous monitoring and periodic fine-tuning of thresholds and settings. As machines wear, as production needs evolve, your system has to be adjusted to keep pace. This helps ensure that the system remains effective, and your asset management remains sharp.

When’s the best time to start with a PdM/CBM Program

 In industrial environments, timing can be everything, especially when it comes to implementing new maintenance strategies. Launching a CBM or PdM program too early, or too late, can mean missed opportunities or wasted investments.

So, when is the right time to invest in predictive maintenance?

Signs You Might Be Ready to Start a PdM Program

(For companies with no or low PdM maturity)

If your organization is still relying on reactive or purely time-based maintenance, here are signs that you might be ready to consider investing in predictive maintenance programs. Well-structured predictive maintenance programs are essential for achieving significant competitive and financial benefits.

Signs You Should Scale or Optimize Your PdM Program

(For companies with an existing CBM or PdM initiative)

Even if you already have aCBM/ PdM program in place, it may be time to revisit your strategy. Asset Here are signs it’s time to optimize or scale up:

Low Failure Prediction Accuracy

If you’re experiencing too many false positives, or worse, missing actual failures, your analytics might need refining or your data inputs could be unreliable.

Poor Integration with Work Management

If condition monitoring findings aren’t triggering work orders or flowing into your CMMS, you’re leaving value on the table. Integration is key to a proactive workflow.

No Measurable ROI

If you’re not seeing reductions in downtime, maintenance costs, or failure rates, it may be time to reassess how your CBM or PdM is deployed and managed.

You’ve Expanded or Added New Critical Equipment

New production lines or capital investments are the perfect opportunity to build PdM into your reliability strategy from day one.

Common Triggers That Justify the Investment

Even when you’re not actively planning a PdM rollout, some events can accelerate the decision and justify immediate investment:

• Post-Major Failure or Loss Event: A costly breakdown that halted production is a wake-up call that could have been avoided with predictive insights.
• After Implementing a New CMMS or ERP: If you’ve recently modernized your systems, you’re in a strong position to build in PdM capabilities and connect your data ecosystem.
• During Digital Transformation or Industry 4.0 Initiatives: PdM aligns well with broader digitization goals, offering quick wins in visibility and performance. Predictive maintenance technologies are essential enablers of Industry 4.0, helping organizations prevent equipment failures and reduce economic losses across sectors.
• Asset Commissioning or Capital Projects: Integrating predictive or condition-based maintenance from day one with new assets is cheaper and more effective than retrofitting later.
• New Safety or Regulatory Pressures: In some industries, condition monitoring and PdM/CBM are no longer optional when safety or compliance is at stake. Predictive maintenance supports service technicians by enabling proactive interventions, optimizing maintenance scheduling, and reducing emergency repairs.
• “This Could Have Been Prevented” Moments: If you’re hearing “we could have avoided this” after a major incident, that’s a strong sign the timing is right.

How to implement a PdM/CBM Program

Developing a robust Condition-based maintenance / Predictive Maintenance program involves several essential steps, each building on the previous one to ensure a structured and effective implementation.

Let’s explore each step in detail.

Step 1: Assess Needs and Priorities

A great CBM/PdM program should begin with a detailed evaluation of your organization’s specific needs and goals. This phase lays the foundation for a targeted and efficient strategy.

Building a successful maintenance strategy starts with understanding your operational landscape. This involves analyzing the criticality of your assets, aligning program objectives with business goals, and evaluating the current state of your maintenance strategy. By laying this groundwork, you ensure that the PdM program addresses the most pressing challenges and adapts to your situation.

Step 2: Select the Right Equipment and Technologies

The tools and technologies you choose for your maintenance program play a crucial role in its effectiveness. This step ensures that your program is equipped with the right resources to achieve its objectives.

Step 3: Integrate CBM/ PdM into Maintenance Processes

To fully realize Condition-based of predictive maintenance benefits, integrate its insights into your maintenance workflows.

Step 4: Train Staff and Promote Buy-In

A PdM program is only as effective as the people who operate it. Ensuring your staff is well-trained and fully invested in the program is crucial for its success.

Step 5: Measure Performance and Optimize the Program

The final step in building a PdM program is to continuously measure its performance and make improvements.

How to Conduct Predictive Maintenance Inspections

As we mention above, CBM and PdM are strategies that can improve your maintenance greatly. But how can you ensure inspections yield the best results?

This section provides a comprehensive overview of best practices, tools, and strategies for successful PdM or CBM inspections.

Why are CBM and PdM Inspections Important

Inspections play a critical role in ensuring operational reliability. They help identify early warning signs of failure, enabling maintenance teams to detect potential issues long before they lead to equipment breakdowns.

Additionally, these inspections provide actionable insights that allow for targeted maintenance activities, optimizing resources and minimizing disruptions.

Key Elements of a Predictive Maintenance Inspection 

Conducting effective predictive maintenance inspections requires several key elements. First, setting clear objectives is critical to the success of these inspections, as they help focus efforts on identifying all potential failure signs. Another essential step is selecting the right assets, which involves concentrating on critical equipment identified through comprehensive risk and criticality analysis.

The use of appropriate tools and technologies is indispensable for effective PdM. One of the key components of a successful PdM strategy is the integration of an APM (Application Performance Management) system. This system helps monitor and optimize the performance of critical assets, enabling proactive interventions.

Along with the APM, various specialized tools and technologies play a crucial role:

Integrating these tools with an APM solution provides a holistic approach to PdM, enabling continuous monitoring, a health dashboard giving insights, and data-driven decision-making.

Finally, determining the appropriate inspection frequency is equally important. Factors such as asset condition, the operational environment, and recommendations from original equipment manufacturers (OEMs) should guide the scheduling. Research indicates that shorter inspection intervals lead to improved detection rates, enabling timely interventions to prevent equipment failures.

Step-by-Step Process for Running Effective Inspections 

To ensure predictive maintenance inspections are effective, make sure you follow these steps:

• Training:  Equip personnel with the necessary skills to use PdM tools effectively.
• Preparing for the Inspection:  Preparation involves gathering all necessary tools and equipment, calibration devices, and protective gear, to ensure a smooth and efficient inspection process. It also includes reviewing historical data and previous inspection results to establish baseline metrics and expectations. Additionally, implementing safety measures like lockout-tagout (LOTO) procedures is essential to protect personnel and equipment, ensuring compliance with safety standards and minimizing risks during the inspection.
• Conducting the Inspection:  Carry out the inspection by following OEE recommendation for each tool.
• Analyzing Inspection Data:  Compare collected data against baseline readings to detect anomalies and trends.
• Taking Corrective Actions:  Prioritize and schedule repairs based on findings. Ensure all necessary tools, parts, and permits are prepared to minimize delays.
• Documenting and Reporting Results:  Maintain accurate records for compliance and continuous improvement.

Best Practices for Success

Maximizing the effectiveness of predictive maintenance requires adherence to these best practices:

• Align PdM with Business Goals:  Ensure PdM efforts support broader operational and financial objectives.
• Integrate PdM with CMMS/APM Software:  Utilize software to streamline planning, tracking, and reporting activities.
• Continuously Monitor and Improve:  Conduct regular audits and update PdM programs based on performance insights.
• Assign Clear Ownership:  Designate a champion to lead PdM initiatives and ensure accountability.

How Predictive Maintenance In Spartakus APM Saved Over $171K

This case study highlights how a pulp and paper facility leveraged cross-referenced oil and vibration analysis within Spartakus APM to save $171,840 by preventing unnecessary repairs and avoiding unplanned shutdown.

Let’s explore the details.

The Warning Signs: Increased Vibration Levels on the Refiner

During a routine bi-weekly inspection, a Laurentide Controls technician noticed a sudden and significant increase in vibration levels on one of the plant’s refiners.

These vibrations corresponded to twice the rotation frequency of the rolling elements and matched a ball spin frequency (BSF) pattern, signaling a potential ball defect in the bearing.

Given the risk of unplanned downtime, the team made the proactive decision to schedule a bearing cartridge replacement. To confirm the root cause of the vibrations, the reliability team dispatched the lubrication team to collect an oil sample from the refiner’s hydraulic system. The results revealed water contamination in the lubricating oil, a critical finding that prompted immediate changes in the corrective action.

The Solution: Predictive Maintenance Technologies Cross-Referenced in Spartakus APM

With insights from Spartakus APM (Asset Performance Management), Laurentide quickly generated a detailed report outlining the issue. While increased vibration was initially flagged as a symptom, the cross-referencing capabilities of Spartakus APM revealed that vibration itself was not the root cause. Instead, the analysis identified water contamination in the lubricating oil as the underlying issue driving the abnormal vibrations.

Replacing the bearing cartridge might have temporarily masked the vibration symptoms, but it would not have addressed the contamination in the lubrication system, leaving the equipment at risk for future damage. By leveraging Spartakus APM, the team prioritized a targeted approach: performing a system flush and an oil change to eliminate the water contamination entirely.

These corrective measures restored the lubrication system to optimal condition. Once the water contamination was resolved, vibration levels decreased significantly and returned to normal operating ranges, confirming the success of the intervention.

By focusing on the true root cause rather than opting for a temporary fix, the team not only resolved the immediate issue but also reinforced the equipment’s durability and operational reliability for the future.

Results: $171,000 Saved Through Early Intervention

By leveraging predictive maintenance and acting on the data, the plant avoided:

• Labor Costs:  The issue would have required mobilizing multiple workers for at least two full days. At an estimated cost of $50 per hour, the total labor cost would have been around $3,840.
• Part Replacement:  The bearing cartridge, valued at $168,000, would have required urgent ordering and replacement.
• Extended Downtime:  Without timely action, water contamination would have accelerated bearing degradation, potentially leading to an unplanned shutdown of the refiner. Avoiding this scenario not only prevented disruption but also allowed the plant to avoid additional losses tied to production downtime.

Overall, these preventive measures saved the company an estimated $171,000, including labor, materials, and lost production costs.

Spartakus APM as the Enabler for better Predictive Maintenance

The success of this intervention highlights the power of predictive maintenance tools in addition with Spartakus APM. By training its teams in the use of oil analysis with vibration analysis in Spartakus APM, Laurentide equipped its staff with a comprehensive view of asset health and analysis reports. This proactive approach allowed the team to:

• Monitor asset health and performance.
• Detect potential issues before they escalated.
• Cross-reference different technologies.

The ability to act quickly and decisively not only reduced costs but also boosted the plant’s confidence in its maintenance strategy.