Condition-based maintenance (CBM) is a maintenance strategy that uses various techniques or devices to monitor the working condition of an asset. The condition data based on indicators of decreasing performance or impending failure determines the maintenance task to perform on the asset.
The CBM process may include the use of scheduled tests, non-invasive measurements, sensor data monitoring of performance data to inform on the condition of the asset.
Data is collected at intervals or continuously during different types of CBM which include:
Temperature: infrared cameras can be used to send temperature data for assets that work optimally at a certain temperature. As the assets begin to degrade or malfunction it may record unusually high temperatures that call for maintenance.
Vibrations: for example, because motors, pumps vibrate when in use, a vibration sensor can be used to detect excessive vibrations when the asset begins to degrade or fall out of alignment.
Ultrasonic: for example detection of deep subsurface defects such as boat hull corrosion
Voltage/Current: for example current readings using ammeters
Particulate Count/Composition: for example oil analysis to determine the asset wear or the size of particle composition in a sample.
Acoustic: provides data to detect gas, liquid or vacuum leaks
Other information could include sensor detected asset pressure, speed flow, strain, and other operational performance.
Overall, the goal of condition-based maintenance is to optimize the tradeoff between replacing parts of an asset before their useful life and increasing availability and reliability of an asset while eliminating unnecessary maintenance activities.
Through CBM you can identify impending asset failure and proactively perform a maintenance activity when it is needed, and not earlier. The result is that assets are put to optimal use for an extended period and maintained before it fails or reduces the level of performance.
A typical CBM has the following features:
Detection Algorithms: alerts the right people about the likely assets failures and otherwise unknown failures.
Diagnostic Algorithms: isolates failures to specific parts, assets or subsystems in a facility.
Prognostic Algorithms: this estimates an asset’s remaining useful life based on past and future operational profiles and mechanics of failure models.
Supervisory Reasoning Algorithms: reconciles conflicting information about an asset and recommends inspections, repairs, parts ordering, and asset shutdown.
You can apply the CBM process to the maintenance activities of assets in all industries, including equipment such as jet engines, marine diesel engines, DoD weapons system and more.
At Conmitto we design algorithms to help you perform CBM and solve complex problems of asset function optimization with unclear input/output relationships; pattern recognition with incomplete data; and failure detections for earliest sign of performance dip.
Reduced maintenance costs: CBM has the advantage of reduced operating costs while increasing the safety of assets that needs maintenance.
Unlike reactive maintenance that can have high-performance costs and preventive maintenance that replaces parts before the end of their useful life, CBM cuts down on costs by performing maintenance only after you observe a decrease in the condition of the asset.
CBM also reduces the cost of asset failures as it is performed on working assets to minimize disruptions in operations and also cuts down on maintenance overtime costs by aiding maintenance scheduling.
Increased system availability: CBM reduces asset failure rates while also increasing system availability by using monitoring data to keep assets working optimally for the longest possible time. This minimizes disruptive asset downtime due to critical failure.
It also reduces the time spent in maintenance as s result increasing asset availability.
Increased system reliability: CBM improves on asset manufacturer’s maintenance intervals and offers a more optimal maintenance interval that aid the reliability of assets.
While minimizing the likelihood of collateral damage to your system, CBM also facilitates planned inventory purchase and reduces the need for emergency spare parts for maintenance.
The immediate high cost of set up: while CBM promises cost savings over time, the direct cost of acquiring asset condition monitoring equipment and data analyses is a drawback for some companies.
There is also the team training cost for effective CBM implementation. Unpredictable maintenance requirements and failure of some condition sensors to work optimally in the operating environment are other drawbacks of CBM.
Also because CBM introduces new maintenance techniques, it can sometimes attract resistance within an organization.
Another drawback is the difficulty in retrofitting old assets with sensors and monitoring equipment.