Considering that unplanned downtime costs industrial manufacturers approximately $50 billion every year, it’s safe to say keeping equipment online should be a high priority. When it comes to maintenance strategies, condition-based maintenance (CBM) and predictive maintenance (PdM) might initially seem like two sides of the same coin. Though when digging deeper, it’s apparent these techniques have distinct functionalities that set them apart.
In this guide, we’ll take a look at the main differences and comparative advantages of predictive maintenance vs. condition-based maintenance and compare them in detail. Let’s dive in!
Comparison between condition-based maintenance and predictive maintenance
The world of maintenance strategy and execution can often seem complex, but it doesn’t have to be. In this section we’ll take a deep dive into two specific maintenance methodologies, comparing them, to better understand which can contribute most effectively to your operational efficiency and bottom line. Here’s how each of these approaches stacks up in terms of some key attributes:
Trigger Method
- Condition-Based Maintenance (CBM): Tasks are performed whenever equipment parameters exceed predefined limits
- Predictive Maintenance (PdM): Work is scheduled based on predictive forecasting of when failures are likely to occur
Data Requirements
- Condition-Based Maintenance (CBM): Mostly real-time sensor data, little historical data required
- Predictive Maintenance (PdM): Extensive real-time and historical data for model training, including operational logs and service histories
Cost to Implement
- Condition-Based Maintenance (CBM): Moderate, including installing sensors and basic monitoring software
- Predictive Maintenance (PdM): Higher, due to advanced technology, AI/machine learning software and skilled data scientists to develop training model
Technology Complexity
- Condition-Based Maintenance (CBM): Moderate, relies on straightforward monitoring systems and rule-based alerts
- Predictive Maintenance (PdM): Higher, involving AI and machine learning as well as advanced analytics for failure prediction
Maintenance Timing
- Condition-Based Maintenance (CBM): Reactive to current conditions, based on when thresholds are reached
- Predictive Maintenance (PdM): Proactive, scheduling maintenance before failure occurs
Accuracy
- Condition-Based Maintenance (CBM): Reliance on fixed thresholds may lead to false positives or negatives
- Predictive Maintenance (PdM): Data-driven models result in more-accurate predictions
Downtime Impact
- Condition-Based Maintenance (CBM): If thresholds are crossed without warning, unplanned downtime can still occur
- Predictive Maintenance (PdM): Scheduling maintenance during planned intervals minimizes downtime
Scalability
- Condition-Based Maintenance (CBM): High, thanks to standardized sensors and monitoring systems
- Predictive Maintenance (PdM): Moderate, due to the need for data infrastructure investment and model retraining
Skill Requirements
- Condition-Based Maintenance (CBM): Technicians require training on monitoring systems and threshold interpretation
- Predictive Maintenance (PdM): Requires data scientists, engineers and technicians trained in the use of AI/ML and analytics
Typical Applications
- Condition-Based Maintenance (CBM): Simple machinery including HVAC systems, pumps and motors
- Predictive Maintenance (PdM): Complex systems including turbines, aircraft engines and manufacturing lines
Failure Detection
- Condition-Based Maintenance (CBM): Detects failures as they occur or immediately before based on current conditions
- Predictive Maintenance (PdM): Predicts failures in advance, allowing time for preventive maintenance measures to be taken
ROI Potential
- Condition-Based Maintenance (CBM): Moderate, reduces downtime but may not prevent all failures
- Predictive Maintenance (PdM): High, reduced downtime and extended equipment life result in significant savings
Defining terms: understanding condition-based maintenance and predictive maintenance
When trying to preserve the longevity and efficiency of your machinery, knowing the best maintenance plan for your specific application(s) is critical. This is why, before we go any further, we’ll define each maintenance strategy and how it works in detail.
Condition-based maintenance (CBM) is a proactive maintenance strategy that involves real-time monitoring of machine conditions or parameters (such as vibration and temperature) to decide when maintenance should be performed. This approach can ensure that any maintenance work carried out is on an “as-needed” basis. By using industrial-grade sensors and performing machine health monitoring, data is collected and analyzed to detect anomalies that could indicate a potential failure.
Predictive maintenance (PdM), on the other hand, uses predictive models and data analysis methods to forecast potential equipment failures before they happen. A predictive maintenance strategy involves analyzing data from sensors and equipment monitoring systems to assess the health of machinery and identifying maintenance issues before they cause bigger problems. With this approach, maintenance tasks can be precisely scheduled (often during non-operational periods) to minimize disruption to your production operations.
When CBM may be optimal:
- Condition-based maintenance may be ideal if you have equipment that could cause catastrophic or severe consequences in case of failure, so they must be monitored closely and regularly.
- You have machines whose performance can be effectively tracked using variables such as temperature, vibration and pressure.
- You have equipment operational in remote locations where real-time condition monitoring can flag necessary corrective maintenance tasks.
- You are planning to implement PdM and want a temporary stopgap solution in the meantime.
- Your equipment is simpler and threshold monitoring may be sufficient for catching potential problems.
When PdM may be optimal:
- You have older equipment with a higher risk of failure; predictive maintenance can help plan for and mitigate these issues.
- Your operation relies heavily on a few key pieces of equipment whose failure would significantly impact productivity.
- Your equipment consistently experiences the same types of failures; predictive maintenance powered by AI and machine learning can help anticipate these issues.
- You operate complex systems that could benefit from analyzing historical data to predict failures.
CBM and PdM in practice: Are they mutually exclusive?
Although these approaches are very different from each other, that’s not to say they are mutually exclusive. Many manufacturers take a hybrid approach that finds niches for each technique. For example, some choose to implement CBM as a tactical tool and PdM as a strategy for achieving long-term reliability. This means they may use CBM to detect sudden spikes in vibrations in specific equipment, with PdM taking the data from that to predict the lifespan of the bearings.
Achieve operational excellence today with ATS’ maintenance approaches
Understanding and implementing the right maintenance approach and knowing the difference between condition-based maintenance and predictive maintenance can be a game-changer for manufacturers.
ATS specializes in both these approaches by delivering industry-leading predictive maintenance services tailored to specific operational needs.
No matter where you are on your journey to operational excellence, partnering with ATS can accelerate your progress. With our unparalleled support and technology-driven services, we can help you navigate the complex world of industrial maintenance. Contact us today!
References
Ravande, S. V. (2022, February 22). Unplanned downtime costs more than you think. Forbes. https://www.forbes.com/councils/forbestechcouncil/2022/02/22/unplanned-downtime-costs-more-than-you-think/
