Condition-based maintenance (CBM) is a preventive maintenance strategy that relies on the monitoring of assets or equipment to determine when maintenance work is necessary.
CBM involves the use of sensors and other monitoring equipment to collect data on the performance of equipment. Using algorithms, machine learning and AI the collected data is then analyzed to identify patterns and anomalies that might indicate a maintenance issue.
In the past, companies only performed maintenance on a fixed schedule or when equipment failed, often resulting in expensive and inefficient maintenance practices (that is, unexpected downtime and emergency repairs). However, condition-based maintenance offers a newer, more advanced approach to maintenance management.
Rather than performing maintenance on a predetermined schedule or waiting for equipment breakdowns, CBM uses real-time data to identify maintenance needs, allowing for more efficient and cost-effective maintenance practices.
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Condition-based maintenance and predictive maintenance are both asset management approaches that can help organizations minimize the likelihood of equipment failure and maximize asset lifespan. However, they differ in some key ways.
With CBM, the maintenance department performs maintenance on an as-needed basis; it’s an inherently reactive process. However, predictive maintenance uses data analysis and machine learning to predict when it’s time to perform maintenance tasks. It represents a more proactive approach to asset management.
Furthermore, CBM relies on inspections, tests and real-time data to assess the current condition of equipment, while predictive maintenance is based on continuous monitoring and data analysis to predict future equipment behavior.
Both approaches can help organizations keep critical assets operating at peak performance, so either approach (or both) might be right for your business. However, choosing the best strategy depends on factors like the type of equipment you have, the criticality of your assets, the industry you operate in, or the environment in which you house the assets.
While there are myriad monitoring techniques for CBM, these are the types of condition-based maintenance you’re most likely to encounter.
Infrared thermography uses thermal imaging to detect overheating and other temperature-related issues. It relies on non-contact measurements to detect potentially problematic temperature variations in objects and surfaces.
Infrared thermography uses thermal imaging cameras to capture the infrared radiation emitted by an object or surface and convert it into a visual image (or thermogram). The thermogram is then used to measure the current asset temperature against the asset’s baseline temperature.
Organizations primarily use this type of CBM to monitor motors, inspect bearings, and check gas, sludge or liquid levels.
Vibration monitoring also known as vibration analysis, uses vibration sensors to measure vibration frequencies in an asset and detect abnormalities that might indicate a problem.
Since rotating assets like motors and pumps, for instance, tend to vibrate more intensely and more loudly as they age, measuring changes in vibration can help identify wear and damage before the asset fails. Vibration monitoring can be used to detect a wide range of problems, including misalignment, imbalance, bearing wear or failure, bent shafts and loose components, among other faults.
Oil analysis assesses the properties of the oil like viscosity and acidity, for example, in an asset to detect contaminants or wear particles. It typically involves collecting a sample of lubricating oil from the equipment and sending it to a laboratory for analysis. Oil analysis can be useful for monitoring assets like engines, gearboxes and hydraulic systems.
Ultrasonic analysis (or ultrasonic testing) uses high-frequency sound waves to detect leaks, cracks or defects in a piece of equipment. It relies on both contact (structure-borne) and non-contact (airborne) data collection techniques to determine asset attrition.
Contact methods are typically used to detect mechanical issues—like lubrication issues, gear damage and broken rotor bars—that generate high-frequency sounds. Non-contact methods can detect issues, like pressure and vacuum leaks in compressed gas systems that tend to generate low-frequency sounds.
Assets that carry gas, air or fluid are best monitored by using pressure analysis, that is, the process of measuring and evaluating the pressure levels within an asset.
Maintenance teams can use pressure analysis to determine the flow rate and velocity of fluids through pipes and valves; to optimize the performance of air compressors and regulators; and to control the pressure of gases and liquids in tanks and pipelines.
Electrical analysis assesses the incoming power quality of electrical systems or components that use motor current readings from clamp-on ammeters. Measurements like voltage, current, resistance, capacitance, inductance and power can help maintenance teams anticipate voltage drops, power factor problems and circuit faults and distortions.
The CBM lifecycle describes the stages of the CBM process, each of which plays an integral role in the overall success of the program. These stages include planning, implementation, monitoring, analysis and improvement.
In the planning stage, the maintenance team should clearly define the objectives of its CBM program. The objectives should align with the overall goals of the organization, and be specific, measurable, achievable, relevant and time bound.
After you lay out your objectives, you should identify critical assets and focus the condition-based monitoring program on these assets. You develop a monitoring plan that outlines the specific monitoring techniques you use, and the frequency and duration of monitoring processes.
The plan should also identify the personnel responsible for monitoring and analyzing equipment performance data. This approach helps to ensure that maintenance departments use resources effectively and keep unplanned downtime to a minimum.
Finally, the maintenance team should establish its baselines in the planning stage. Baselines are an essential component of CBM, as they provide a reference point for measuring changes in equipment condition and help you identify patterns in asset behavior.
You can use operating baselines, which reflect the typical operating conditions of assets; historical baselines, which are based on assets’ historical data; manufacturer baselines, which are established by the manufacturer of the equipment; or any other baseline metric the maintenance department finds useful.
In the implementation stage, the team installs the sensors and data acquisition systems and trains personnel to use the CBM tools. This stage requires the team to develop a data management system and integrate the CBM into the organization's maintenance management system.
The monitoring stage is the most vital part of any CBM program. It involves collecting data from the sensors and data acquisition systems, ideally on a continuous basis, to monitor the condition of the equipment in real time.
In the analysis stage, the team interprets the data collected during the monitoring stage, either manually or by using software tools. This includes identifying patterns and trends, and detecting anomalies and potential failures.
Once the team interprets the results of the analysis, it will develop and implement and action plan. This might include scheduling maintenance activities, adjusting operating parameters or making improvements to the equipment or the monitoring system itself.
The team will also document the results of the CBM program and incorporate them into future planning and implementation activities.
It's important to note that the CBM lifecycle is not a one-time process, but rather a continuous cycle. As such, the success of a CBM program depends on whether/how an organization continuously improves and refines its approach to maintenance.
Condition-based maintenance is an effective strategy for improving equipment reliability and reducing maintenance costs. However, it can only be effective when the condition-based management program is well-designed and well-executed. Here are some best practices that will help your organization optimize the CBM process.
Understanding p-f intervals and p-f curves can be useful in predictive maintenance programs because they help determine the optimal timing of maintenance activities.
The p-f curve provides a visual representation of the relationship between the severity of a fault and the time to asset failure. By analyzing the p-f curve for a particular piece of equipment, it is possible to identify the most critical faults and prioritize maintenance activities according to criticality.
The p-f interval, however, shows the time available to perform maintenance when an impending failure is detected. The p-f interval can help maintenance personnel schedule maintenance activities in advance, before equipment fails.
The CBM process generates large amounts of data that needs to be stored, analyzed and acted upon in a timely manner. Data management and analysis software can help make sense of the data and turn it into actionable insights.
Also, many CBM programs are compatible with computerized maintenance management systems (CMMSs) and enterprise asset management (EAM) systems, making integrating CBM into your existing asset management program simpler.
CBM programs require maintenance teams to schedule maintenance based on the actual condition of equipment. To do this effectively, organizations need to implement a maintenance scheduling system that can account for performance data and prioritize maintenance tasks accordingly.
Condition-based maintenance is not a one-time implementation, but rather a continuous process of monitoring, analysis and improvement. Organizations should continually encourage maintenance personnel to identify areas of improvement and implement changes based on the results.
Condition-based maintenance is an effective strategy for improving equipment reliability and lifespan. The primary benefit of CBM is its lean approach to asset management, but it offers other, more nuanced benefits as well.
Prevents equipment failures and downtime: By detecting potential issues and impending failures early on, CBM enables a maintenance team to schedule maintenance at its convenience, when it’s the most cost-effective. This can help reduce costs associated with emergency maintenance and repair, minimize production schedule delays, and maximize asset uptime.
Extends asset lifespan: By performing maintenance regularly, based on real-time data, maintenance personnel can keep equipment in optimal condition, reducing wear and tear and extending asset lifespan.
Improves safety: Condition-based maintenance can help detect issues that might become safety risks down the line, allowing maintenance to take corrective action before an accident occurs, and ultimately reducing workplace injury and accident risk.
Reduces maintenance costs: Rather than performing maintenance on a predetermined schedule, CBM allows teams to perform maintenance tasks only when necessary, effectively reducing unnecessary maintenance costs.
Improves maintenance efficiency: CBM helps streamline maintenance practices by reducing the time and resources required for maintenance, and improving the accuracy of maintenance practices.
Condition-based maintenance programs empower organizations to develop proactive maintenance plans, but there can be challenges with implementation.
High implementation costs: One of the primary challenges is the need for specialized equipment and expertise. Implementing CBM requires the use of advanced sensors and monitoring equipment, and software and algorithms to analyze asset data.
This can be costly upfront, but the long-term benefits of CBM usually outweigh the initial investment. Also, technological advances have made CBM programs more affordable, making them more accessible to a wider range of organizations.
Extensive data collection and analysis: CBM relies on real-time diagnostics to inform decision-making and maintenance practices, so data must be collected and analyzed on an ongoing basis. This requires a robust data collection system and maintenance software capable of analyzing large amounts of data as it populates. Furthermore, personnel must be trained to interpret the data and take appropriate steps based on the results, so companies looking to adopt condition-based maintenance strategies should prepare accordingly.
Complex integration process: To be optimally effective, CBM must be integrated with existing systems and equipment. This can prove difficult in industries with older equipment or legacy systems that are incompatible with modern monitoring equipment. Some organizations might ultimately need to retrofit existing equipment with new sensors and monitoring equipment or upgrade systems to ensure compatibility with CBM. They can also evaluate solutions that provide connectors to facilitate integration with legacy systems.
Significant data security considerations: CBM relies on extensive data collection and storage, which can raise concerns about data security. It is important to help ensure that data is stored securely and that access to sensitive data is restricted to authorized personnel only.
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