In the dynamic landscape of industrial operations, the traditional approach to maintenance has evolved into a more proactive and data-driven strategy known as Condition-Based Maintenance (CBM).
In this blog, we will delve into the core principles of condition-based monitoring along with the major types of techniques used in it. We will also explore the difference between CBM and predictive maintenance as types of maintenance strategies.
What is condition-based maintenance?
CBM maintenance is a technique where equipment is monitored in real time to indicate failures. CBM strategies overlap with predictive maintenance, but the two are different in their approach.
Tracking asset performance is what predictive maintenance does. However, CBM relies on continuous asset monitoring to trigger maintenance actions rather than keeping a fixed schedule. The goal here is to check asset performance continuously to do maintenance as and when the equipment performance drops below a certain level. This prevents breakdowns and saves costs.
CBM strategies use condition-based maintenance tools like sensors that monitor the real-time performance data of the asset. Data such as temperature, humidity level, pressure, vibration, etc. are impending causes of asset failure that sensors can detect in case of a decrease in performance. Condition-based monitoring uses different techniques which differ based on asset and production level.
Benefits of adopting CBM strategies
- Reduction in downtime.
- Elimination of unplanned failures.
- Reduced maintenance costs.
- Increased asset life.
- Reduction in collateral asset damages.
- Enables better asset management across its life span.
- Provides the basis for formulating predictive algorithms for the future.
Types Of Condition-based Monitoring
Condition-based maintenance techniques vary from simple inspections to cutting-edge technology. Depending on the equipment type used for the production stage, there are several CBM types.
The most common types of condition-based maintenance are:
1. Vibration analysis
Vibration analysis is one of the most commonly used maintenance methods which utilizes vibration sensors. It can detect misalignment, wear, and imbalances 3 months prior they can cause a breakdown.
Vibration analysis is especially instrumental in assessing the health of rotating machinery, as they tend to vibrate more when aging, thus affecting the performance. The way assets respond to vibration may assess when and where maintenance is required. Vibration analysis includes various techniques, such as shock pulse analysis and broadband vibration analysis.
2. Oil analysis
Oil is a critical component in any equipment and thus it is reasonable to analyze the oil to get insights about the asset's performance. It can detect wear, overheating, and contamination. More iron content often means dirt and grit, which, if spotted on time, reduces the gearbox failures by 50%.
By checking the oil properties, the maintenance team can predict potential asset failure, optimize lubrication practices, and increase the equipment life span. Most common soil monitoring methods include water presence tests, ferrography, viscosity tests, microbial analyses, sediment tests, etc.
3. Infrared thermography
This condition-based monitoring uses a thermal camera that simply detects temperature variations produced by equipment for analysis. The maintenance team usually keeps ideal temperature levels set for all critical assets, which can be compared with the current analysis. If the temperature exceeds the optimum level, the maintenance team will be alerted for a potential threat.
4. Ultrasound testing
Ultrasound testing focuses on detecting high-frequency sounds emitted by equipment during operation. This technique is particularly effective for identifying issues such as leaks, electrical problems, or bearing defects.
Ultrasound testing allows for the detection of problems in their early stages, preventing costly and unplanned downtime. This CBM strategy is especially cost-effective if used in combination with vibration analysis and infrared thermography.
5. Electrical monitoring
Equipment can pull varied amounts of electricity throughout its life span and performance. Faults in machinery can also be prevented by conducting a thorough electrical analysis which not only avoids breakdowns but enhances safety as well.
This condition-based monitoring tool includes tests to assess resistance, induction, capacitance, pulse response, frequency response, and degradation. When the current electrical reading is taken, the maintenance team gets notified which asset is pulling more electricity than required.
6. Pressure monitoring
Some assets need to have gas, air, or fluid flowing throughout the system. Poor pressure would mean loss of performance and too much pressure can be life-threatening to employees.
Some sensors can measure pressure around the clock. Alert will be sent to the maintenance team if the pressure ranges more than the optimum value, so that predictive measures can be taken to avoid failures.
7. Radiation analysis
Similar to how an X-ray is performed, radiation analysis thoroughly checks an asset from the inside out. It is one of the most thorough methods of non-destructive testing. This method looks at how much radiation is being consumed by the component being tested.
Flaws such as corrosion in the internal wiring may not be visible from the naked eye but they tend to absorb more radiation, thus getting them detected.
Predictive Vs. CBM Maintenance: How Do They Differ?
Predictive Maintenance (PdM) and Condition-Based Maintenance (CBM) are both proactive approaches to maintenance strategy that leverage data and technology to optimize equipment reliability and minimize downtime. While they share similarities, they differ in their methodologies and key principles.
| Comparison Points | Predictive Maintenance | Condition-based Maintenance |
|---|---|---|
| Approach to Failure Prevention | PdM predicts future failures through data analysis. | CBM primarily monitors the state of affairs and, in response to detected deviations, initiates maintenance procedures. |
| Data Utilization | For predictive modeling, PdM uses machine learning and sophisticated analytics. | CBM uses sensor data collected in real time to evaluate the state of the machinery. |
| Timing of Maintenance Actions | PdM schedules maintenance activities based on predictions | CBM triggers maintenance actions as conditions warrant, leading to a more immediate response. |
| Infrastructure Requirements | A strong data infrastructure is necessary for modeling and analysis in PdM. | Systems for continuous monitoring of particular parameters are necessary for CBM. |
| Use Case | PdM is widely used in the railway sector, where IoT-based sensors are installed on trains to detect issues before they become serious. | When equipment overheats, manufacturing facilities utilize infrared cameras to give out alerts. |
In practical usage, both approaches can be used by organizations based on the specific needs and how critical the assets are. Predictive Maintenance is well-suited for assets where failure patterns can be identified, while Condition-Based Maintenance is effective for assets where real-time monitoring of specific parameters is crucial.
There are different types of condition-based maintenance that the companies can choose from based on the requirements. Some may be costlier than others, but in the long run, they help in reducing costs by limiting the machinery breakdown.
