Introduction
In large businesses where asset management includes a significant amount of maintenance. You may save a lot of time and money by using the proper maintenance. With careful planning and maintenance, you may avoid the majority of potential downtimes. Predictive maintenance and condition monitoring maintenance are two of the most sophisticated asset maintenance strategies. Many individuals employ these two maintenance methods in some capacity.
There is, however, a significant difference between them. What is the distinction?
The difference between these two methods is how they predict possible failures, and what steps should be taken to prevent them from happening.
What is Condition-based Maintenance?
Condition-based maintenance (CBM) is a maintenance method that analyses an asset’s real condition to determine what repair is required. According to CBM, maintenance should only be undertaken when specific indicators indicate a decline in performance or an impending breakdown. Non-invasive measures, visual examination, performance data, and planned testing may be used to check a machine for these signs. Alternatively, condition data can be collected continuously or at predetermined intervals (such as when machines have internal sensors). Condition-based maintenance may be used on assets that are both mission essential and non-mission critical.
It collects real-time readings (such as pressure, temperature, or vibration) on a piece of equipment using sensor devices. CBM data enables maintenance professionals to do maintenance precisely when it is required, prior to failure.
The simplest straightforward example is the age-old visual examination. Technicians move throughout the plant looking for little indications of potential issues. Water or oil puddles Strange clunking noises There is steam where there should not be any. The more technological examples are also the most recent. Technicians perform periodic checks with portable equipment or install sensors for continuous monitoring. Vibration, infrared, oil analysis, pressure, temperature, and flow are all common types.
What is predictive maintenance?
Predictive maintenance (PdM) is a preventive maintenance program that uses data analysis to predict when equipment will fail. PdM is based on the concept that equipment failure can be predicted and avoided by conducting regular maintenance activities before it becomes necessary. This can save you from having to replace your machinery or equipment, which may be more expensive than simply maintaining it at its current level of performance.
With predictive maintenance, equipment downtime and maintenance expenses are reduced while unplanned reactive maintenance is eliminated. Predictive maintenance increases the lifespan of the object being monitored.
It evaluates asset performance using monitoring devices. The integrated sensors capture a wide range of data from every piece of equipment (critical or not), such as temperature, vibrations, and pressure, among others. The Internet of Things (IoT) is a highly critical component of the process because it enables multiple systems to collaborate to translate and analyze recorded data to predict when maintenance should be conducted. Furthermore, machine learning technology enables prediction algorithms to improve their accuracy over time.
As bandwidth and storage costs are low, very large volumes of data can be captured and analyzed to gain an overview of a whole production network, rather than just assets in one plant. Many sectors are keen to use AI-based PdM to obtain improved insights into operations with the introduction of Industry 4.0.
Condition-based maintenance vs Predictive maintenance
- Predictive maintenance primarily makes use of sensor data to predict when maintenance is required. Condition-based maintenance likewise uses sensors, but it can only warn you when your equipment begins to malfunction.
- Predicted failure scenarios initiate predictive maintenance, whereas an asset’s exact status initiates condition-based maintenance.
- The cost savings involved in Predictive maintenance will result in savings of up to 30%. However, the cost reductions in condition-based maintenance depend on the number of assets.
- Prediction maintenance combines condition-based diagnostics (the measurement of vibrations, temperature, and other variables with a predictable pattern) with complicated prediction formulae to precisely anticipate when a piece of equipment will break. CBM is based on fixed intervals and lacks the predictive algorithms needed to evaluate various patterns.
Predictive maintenance is, in some ways, a more accurate form of condition-based maintenance.
What are the advantages of condition-based maintenance?
The following are the overall advantages of condition-based maintenance:
- Reduces the cost of asset failure.
- Enhances asset productivity.
- Reduces unforeseen failure downtime.
- Reduces the amount of time spent on repair and maintenance following a failure.
- Improved maintenance procedures and management efficiency.
What are the advantages of predictive maintenance?
- Unscheduled equipment downtime caused by equipment or system failure is reduced or eliminated.
- labor utilization is increased.
- production capacity is increased.
- maintenance expenses are reduced.
- equipment lifespan is extended.
Conclusion
Now that you have an idea of what each type of maintenance is, it’s time to start thinking about how you use it to achieve operational excellence. Sensors are used in advanced maintenance procedures to monitor changes in equipment vibration, temperature, ultrasounds, and other parameters. Furthermore, both PdM and CBM programs can aid in the reduction of costly equipment failures. Because of their high setup and operational expenses, maintenance techniques are best suited for big enterprises with substantial resources. PdM technology, on the other hand, takes efficiency a step further by integrating real-time monitoring with predictive algorithms that assist reliability engineers to pinpoint the exact optimal times for asset maintenance. If you’re looking for ways to improve your plant operations with an AI-based PdM solution, contact us!