Condition based maintenance

Everything you need to know about condition based maintenance

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What is condition-based maintenance (CBM)?

Condition-based maintenance (CBM) is a strategy that monitors the actual condition of an asset to decide what maintenance needs to be done. CBM dictates that maintenance should only be performed when specific indicators show decreasing performance or upcoming failure. Checking a machine for these indicators may include non-invasive measurements, visual inspection, performance data, and scheduled tests. Condition data can then be gathered at specific intervals or continuously (as is done when a machine has internal sensors). Condition-based maintenance can be applied to mission-critical and non-mission-critical assets.

What’s the goal of condition-based maintenance?

Condition-based maintenance aims to monitor and spot upcoming equipment failure so maintenance can be proactively scheduled when needed–and not before. Asset conditions need to trigger maintenance within a long enough time before failure, so work can be finished before the asset fails or performance falls below the optimal level.

For condition-based maintenance to succeed, several other elements of your maintenance operation need to be in place. That includes having a scheduled maintenance strategy that allows you to inspect and spot anomalies in equipment and trigger timely follow-up work orders. If you want to take the next step and predict which work orders will lead to asset failure, check out what AI-powered work order reports can do for you. It’s also essential to have the right parts and supplies on hand when problems in performance are identified, and work is created. Read more about forecasting your parts using historical data and artificial intelligence.

Condition-based maintenance workflow

A condition-based maintenance (CBM) workflow outlines the steps to manage CBM activities. It provides a structured approach to effectively monitor equipment conditions, analyze data, and perform maintenance actions based on the equipment's health. Here's an example of a typical workflow for condition-based maintenance for Fiix:

Diagram of a condition-based maintenance workflow

As shown in the example above, Asset A has a series of sensors and tags connected to it. The data from these sensors and tags get aggregated and streamed to an API and the cloud (in this case, a CMMS). At that point, an AI sets up a training period to understand the baselines of the asset. Generally, the training takes some time, and this step takes about one week with Fiix's CMMS. From here, the AI predicts risks and then displays the them on a dashboard for the asset and, lastly, creates a maintenance task and work order for the asset if required.

What is the difference between condition-based and predictive maintenance?

Condition-based maintenance (CBM) and predictive maintenance (PdM) share some similarities but are used in different ways and for different purposes. Below is a table that illustrates some of the differences:


Condition-based maintenance (CBM)

Predictive maintenance (PdM)

Use case

CBM involves monitoring the current condition of equipment or systems using various sensors, measurements, and data collection techniques. Maintenance actions are then scheduled based on the observed condition or predetermined thresholds.

PdM uses advanced data analysis techniques and predictive models to estimate when maintenance should be performed. It combines real-time and/or historical data with algorithms to predict equipment failure or performance degradation.

Data utilization

CBM relies on real-time or periodic measurements and observations of the equipment's health and performance parameters. This data is used to make decisions about maintenance actions.

PdM utilizes historical and real-time data, including sensor data, maintenance records, and other relevant information. Advanced analytics and machine learning algorithms are applied to this data to predict future failures or performance issues.

Maintenance triggers

Specific condition indicators or thresholds typically trigger maintenance actions in CBM. For example, maintenance might be scheduled when a certain vibration level is reached, or a particular parameter falls outside a predefined range.

Maintenance actions are triggered based on predictions or estimates of future equipment failure or performance degradation. Algorithms analyze data patterns and identify trends that indicate the need for maintenance.

Time and cost efficiency

CBM aims to optimize maintenance efforts by performing maintenance activities only when a problem is clearly indicated. This can reduce unnecessary maintenance and associated costs.

PdM aims to minimize unplanned downtime by predicting equipment failures in advance. By addressing issues before they lead to failure, PdM can improve overall uptime and reduce maintenance costs.

In addition to these differences, it's essential to understand that the predictive maintenance workflow in a CMMS is very different from the condition-based workflow.

Predictive maintenance workflow

A predictive maintenance (PdM) workflow typically involves several stages to implement the strategy effectively. Here's an example of a typical workflow for predictive-based maintenance for Fiix:

Diagram of a predictive maintenance workflow

As shown in the example above, Asset B has a series of sensors and tags connected to it. The sensor and tag data then gets linked to thresholds that the maintenance manager (or other maintenance personnel) defines. From here, if the meter for a specific sensor or tag is over (or under) the set threshold, the information is sent through the API into the cloud (in this case, a CMMS). When this data is sent into the CMMS, a trigger may (or may not) occur based on the data. If the trigger indicates an issue with Asset B, a maintenance task is created, and a work order is assigned to fix the problem.

What are the advantages and disadvantages of condition-based maintenance?

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  • CBM is performed while the asset is working, which lessens the chances of disruption to normal operations
  • Reduces the cost of asset failures
  • Improves equipment reliability
  • Minimizes unscheduled downtime due to catastrophic failure
  • Minimizes time spent on maintenance
  • Minimizes overtime costs by scheduling the activities
  • Minimizes requirement for emergency spare parts
  • Optimizes maintenance intervals (more optimal than manufacturer recommendations)
  • Improves worker safety
  • Reduces the chances of collateral damage to the system
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  • Condition monitoring test equipment is expensive to install, and databases cost money to analyze
  • Analyzing the collected data and generating actionable insights often requires specialized knowledge and expertise
  • There will be costs to train the staff once a knowledgeable professional analyzes the data and performs the work
  • Fatigue or uniform wear failures are not easily detected with CBM measurements
  • Condition sensors may not survive in the operating environment
  • May require asset modifications to retrofit the system with sensors
  • Unpredictable maintenance periods

How to make conditioned-based maintenance more effective

Optimize your CBM program

Example of condition-based maintenance

Motor vehicles come with a manufacturer-recommended interval for oil replacements. These intervals are based on manufacturers’ analysis, years of performance data, and experience. However, this interval is based on an average or best guess rather than the actual condition of the oil in any specific vehicle. The idea behind condition-based maintenance is to replace the oil only when a replacement is needed and not on a predetermined schedule.

Oil analysis can perform an additional function in the example of industrial equipment. By looking at the type, size, and shape of the metal particulates that are suspended in the oil, the health of the equipment it is lubricating can also be determined.

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Types of condition-based maintenance monitoring techniques

Vibration analysis

Vibration analysis







Oil analysis

Oil analysis



Operational performace

Operational performance

There are various types of condition-based monitoring techniques. Here are a few common examples:

  • Vibration analysis: Rotating equipment such as compressors, pumps, and motors all exhibit a certain degree of vibration. As they degrade or fall out of alignment, the vibration increases. Vibration sensors can be used to detect when this becomes excessive.
  • Infrared: IR cameras can detect high-temperature conditions in energized equipment.
  • Ultrasonic: Detection of deep subsurface defects such as boat hull corrosion.
  • Acoustic: Used to detect gas, liquid, or vacuum leaks.
  • Oil analysis: Measures the number and size of particles in a sample to determine asset wear.
  • Electrical: Motor current readings using clamp-on ammeters.
  • Operational performance: Sensors measure pressure, temperature, flow, etc.

How is condition-based maintenance data collected?

Two different methods can collect data:

  1. Spot readings can be performed at regular intervals using portable instruments
  2. Sensors can be retrofitted to equipment or installed during manufacture for continuous data collection

Critical systems that require considerable upfront capital investment or could affect the quality of the product produced need up-to-the-minute data collection. More expensive systems have built-in intelligence to self-monitor in real time. For example, sensors throughout an aircraft monitor numerous systems in flight and on the ground to help identify issues before they become life-threatening. Typically, CBM is not used for non-critical systems, and spot readings will suffice.

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How to create a condition-based maintenance program

Creating a condition-based maintenance (CBM) program involves several key steps. Here's a general framework to guide you through the process:

  1. Define objectives and scope: Identify the goals and scope of your CBM program. Determine which equipment or assets will be included and the specific objectives you aim to achieve. This could be reducing downtime, optimizing maintenance costs, improving equipment reliability, or maximizing asset lifespan.
  2. Identify your critical equipment: Identify the equipment or assets that are critical to your operations and have a significant impact on productivity or safety.
  3. Select appropriate condition monitoring techniques: Evaluate and select the most suitable methods based on the equipment and failure modes. Standard practices include vibration analysis, thermal imaging, oil analysis, acoustic, and performance monitoring. Choose techniques that provide meaningful data on the equipment's health and performance.
  4. Establish your baseline data and thresholds: Collect initial baseline data to establish each monitored asset's normal operating conditions and performance levels. Determine appropriate thresholds or alarm limits for different parameters to indicate when maintenance actions are required. These thresholds can be based on historical data, equipment specifications, industry standards, or expert knowledge.
  5. Implement data collection and analysis: Set up a system to collect, store, and analyze the data from the condition monitoring sensors. This can involve using software like a CMMS or specialized data analysis and visualization tools. Develop algorithms or rules that trigger alerts or work orders when the monitored parameters exceed the defined thresholds or show signs of deterioration.
  6. Develop your maintenance strategies: Using the analyzed data and identified asset conditions, determine appropriate maintenance actions. This can range from corrective actions responding to imminent failures to preventive or predictive maintenance tasks. Consider factors such as the criticality of the asset, cost-benefit analysis, and resource availability when developing maintenance strategies.
  7. Establish workflow and responsibilities: Define the workflow and responsibilities for executing maintenance tasks based on the CBM findings. Clearly outline the roles and responsibilities of technicians, engineers, and data analysts involved in the CBM program. Develop procedures for documenting and communicating the findings, work orders, and maintenance history.
  8. Monitor and refine the CBM program: Continuously monitor the performance and effectiveness of your CBM program. Analyze the data, track key performance indicators, and measure the program's impact on uptime, maintenance costs, and asset reliability.

Use this information to refine and improve the CBM program over time, adjusting the monitoring techniques, thresholds, or maintenance strategies as needed.

Challenges of a condition-based maintenance program

There may be some challenges to effectively running a condition-based maintenance program, and they are important to understand in order to overcome:

  • CBM requires an investment in measuring equipment and staff up-skilling, so the initial implementation costs can be high. The way to overcome it is by getting buy-in from your leadership team to push the use case forward.

    Discover how to combine data and storytelling to get your maintenance project approved

  • CBM introduces new maintenance techniques, which can be challenging to implement due to organizational resistance. The way to overcome it is by training the maintenance team and setting up check-in meetings to make sure the new techniques are being done correctly.
  • Older equipment can be challenging to retrofit with sensors, and monitoring equipment can be difficult to access during production to spot measure. The way to overcome this is by accepting that some monitoring might not be perfectly done from the get-go. It's okay to update and change monitoring techniques as you go through your CBM process.
  • It still requires competence to turn performance information from a system into actionable proactive maintenance items. The way to overcome this is through knowledge acquisition, one of the best ways to learn is from someone who's already done it.

    Join the Fiixer community to get insight from experts into CBM

Condition-based maintenance helps cost save while giving a higher system reliability

When carried out correctly, condition-based maintenance is a minimally disruptive form of maintenance that lessens overhead costs, risk to workers, and downtime due to unexpected breakdowns. Maintenance managers should be aware, however, that setting up a CBM program can be costly, and the changes required to set up the strategy could be met with resistance or confusion. At the end of the day, this strategy requires a great deal of expertise to analyze the data and condition information presented.

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