Condition-Based Maintenance

Every maintenance professional is looking for ways to reduce costs, minimize unplanned downtime, prevent equipment failure, and extend the life of each critical asset. One equipment maintenance strategy to help achieve these goals is condition-based maintenance (CbM).

What is condition-based maintenance?

Condition-based maintenance is any proactive service performed on a critical asset after identifying a sign of poor machine health or anticipated failure. Rather than servicing equipment on a regular time interval or after a breakdown has already occurred, a CbM maintenance strategy relies on evidence found from visible inspection, sensor alerts, or testing to proceed with service.

Condition-based maintenance examples:

  • Changing vehicle tires because the remaining tread is too low.
  • Inspecting machinery because it is running too hot.
  • Changing HVAC filters because it is displaying reduced airflow.

Condition-based maintenance vs. preventive maintenance

Condition-based maintenance (CbM) and preventive maintenance (PM) are both common forms of proactive maintenance and can be easily confused with one another. The most significant difference is that PM relies on a time-based or meter-based schedule for maintenance activity, whereas CbM relies on evidence of equipment failure. Each maintenance strategy comes with its pros and cons.

Evidence-triggered Time-triggered
Variable schedule Consistent schedule
Optimized Estimated

Condition-based maintenance disadvantages

Condition-based maintenance, because it requires evidence to trigger service, is less consistent than preventive maintenance. PM activity will happen on a set maintenance schedule, whereas CbM requires identified conditional triggers before initiating work.

If poor testing misses a vital piece of evidence, CbM strategies won’t service the equipment, whereas a PM schedule will. It’s also much easier to anticipate and plan for preventive maintenance tasks as they will occur regularly.

Condition-based maintenance advantages

The primary benefit to condition-based maintenance is that teams only perform CbM related services when truly necessary. While maintenance teams try to estimate PM service schedules as accurately as possible, they may still perform unnecessary maintenance activities because they don’t consider factors such as lack of usage, ideal operating environment, or well-constructed components.

On the other hand, PM schedules may come too late after an asset has failed because it couldn’t factor in early signs of deterioration. CbM is optimal because teams only perform service when evidence supports it, leading to cost savings in spare parts and labor and reduced unplanned downtime compared to preventive maintenance.

What is condition-based monitoring?

The only way to trigger condition-based maintenance is through signs of potential failure or evidence of reduced performance. The process for measuring, testing, and identifying the current state of an asset to find these triggers is called condition-based monitoring.

As machine health deteriorates to functional failure, various warning signs will present themselves. Condition monitoring aims to identify these signs as early as possible to reduce maintenance costs and increase the time to remedy the problem. The P-F curve is a common way of visualizing machine health as it nears the end of its life and the warning signs it will display along the way.

P-F curve showing potential failure and functional failure

The P-F interval represents when a warning sign is identified (Potential failure) and the time remaining before the asset breaks down (Functional failure). The wider the interval, the more time your maintenance technicians have to respond and the less you will have to spend to remedy the problem. However, the symptoms displayed early on in the P-F curve are more subtle and difficult to detect.

Condition-based monitoring techniques

There are two types of condition monitoring techniques for identifying early signs of failure: interval or continuous. Each technique category offers its advantages, and often they can be used together within the same organization.

Interval monitoring (spot testing)

This style of condition monitoring involves spot testing at regular time intervals. It requires a maintenance professional to manually inspect the asset and schedule service if it meets specific criteria. These spot tests can be a simple visual assessment or leverage specialized testing equipment to uncover detailed data.

The most significant advantage of interval monitoring over continuous is that it’s less expensive, but it is not as reliable and requires timing the tests in a way that catches symptoms before the asset fails.

Below are some spot testing examples.

  • Looking for signs of deterioration
  • Evaluating oil quality
  • Taking a temperature reading
  • Measuring air output
  • Listening for sound inconsistency

Continuous monitoring (sensor alerts)

This style of condition monitoring involves sensors that continuously measure certain variables and send alerts when they cross a predetermined threshold. These sensors can be fundamental, such as a thermostat, or specialized, such as ultrasonic sensors. The advantage of continuous is that it constantly assesses the condition, alerting the maintenance team as soon as a symptom presents itself. It can also be more precise, measuring very subtle signs of failure, but the more specialized the sensors become, the higher the price tag.

Below are some examples of different sensor monitoring techniques.

IoT sensor analysis: oil analysis, partial discharge analysis, emissions testing, vibration analysis, ultrasonic acoustic monitoring, infrared thermography

Oil analysis

Oil analysis can provide insight into the health of your assets’ engines and alert you to issues before they lead to equipment failure. Sensors analyze the oil in your equipment and look for water particles and other liquids, contaminants, and even small bits of metal. The presence of these particles in your oil may mean there is a leak somewhere, or your equipment is wearing.

One of the reasons many organizations utilize oil analysis is because it’s easy to create an oil level baseline when you purchase new equipment.

Partial discharge analysis

Partial discharges are tiny electrical sparks that appear in the electric insulation of switchgear, cables, transformers, and windings in large motors and generators. Similar to other analyses, measurements set outside predetermined discharge parameters create alerts for your maintenance technicians.

Emissions testing

Emissions testing evaluates the level of air pollutants emitted from the exhaust of a piece of equipment or motor vehicle. The sensors look for hydrocarbons, nitrogen oxide, carbon monoxide, and carbon dioxide levels during inspections. If the exhaust is producing excess pollutants, your maintenance staff should take steps to understand and remedy the issue.

The cause of excess air pollutants varies from faulty fuel injection to a broken air injection system to oxygen sensor malfunction and ignition system defects. An emissions test allows your maintenance staff to discover the root cause of the issue before functional failure occurs.

Vibration analysis

Vibration analysis is the process of measuring vibration levels and frequencies of equipment to determine the performance of an asset and its components. Vibration monitoring with sensor measurements can detect discrepancies in the data and flag an alert to maintenance personnel when it crosses a certain threshold.

Ultrasonic acoustic monitoring

Ultrasonic acoustic monitoring utilizes a sensor to detect sounds caused by the rubbing together of components within a bearing. The sounds produced by worn and under-lubricated equipment are not detectable by the human ear. Still, sensors can detect these high pitches and transform them into visual or audible alerts for maintenance staff.

Infrared thermography

Infrared thermography is a method that detects infrared energy released from an asset, converts it to temperature, and displays an image of how temperature disperses over time for that particular asset. As components of your equipment become overused and worn, they tend to release more energy than usual. Higher temperatures and “hotspots” appear on the infrared image, which is easy to identify and makes infrared thermography a popular condition monitoring strategy.

Condition-based monitoring vs. predictive maintenance

Another common proactive maintenance strategy is predictive maintenance (PdM). It overlaps with condition-based monitoring and CbM in many ways, and so it’s worth exploring the similarities and differences. Predictive is an evolution of condition-based monitoring that combines sensor data with an algorithm to estimate when an asset will fail.

Testing-based Prediction-based
Sensor alerts Data algorithms
Variable cadence Anticipated cadence

In a way, PdM brings together the advantages of both condition-based maintenance and preventive maintenance by combining the accuracy of conditional data with the predictability and consistency of preventive maintenance schedules. PdM can anticipate how long you have before an asset will fail and allow your team to schedule maintenance work at a time that makes sense for your operation.

Read more about predictive maintenance.

How is condition-based maintenance implemented?

Because anyone can implement a condition-based maintenance program at various levels, the way to get started can vary depending on your budget, asset reliability, and maintenance personnel. Regardless of your restraints, here are the basic steps for rolling out a CbM program at any level.

Step 1: Select your assets

CbM starts with identifying which assets require monitoring and this type of maintenance—as with many initiatives, starting with your highest priority assets first provides the greatest return on the time, effort, and resources spent on monitoring them.

A recommended technique for prioritizing your assets is failure mode and effects analysis (FMEA). Through this analysis, you can sort your assets by a risk priority number (RPN) built by three factors:

  1. Severity: how costly would it be if this asset were to fail?
  2. Occurrence: how likely is this type of failure mode?
  3. Detection: how possible is a missed detection of this failure?

Step 2: Determine failure indicators

After selecting your assets, the next step is to determine what indicators to look for with each failure mode. We recommend choosing which indicators to monitor (temperature, vibration, noise, oil quality, emissions, etc.) with set thresholds to trigger maintenance work (if the temperature rises above a particular degree or the noise exceed a specific dB rating).

Step 3: Establish monitoring techniques

With a list of assets and failure indicators in place, now you will have the challenging task of determining what level of monitoring efforts make sense for your budget and maintenance personnel. Below are a few considerations.

Spot testing Sensor alerts
Low barrier of entry Upfront investment
Periodic testing Remote monitoring
Requires maintenance team bandwidth Requires sensor purchase
Subject to human error Subject to technology error
Better at identifying subjective indicators Better at identifying subtle indicators
Requires manually submitting a work order Requires software integration to submit a work order
Requires specialized equipment for detailed tests Requires specialized sensors for thorough monitoring

Step 4: Execute a follow-up process

With monitoring in place, the final step is to execute the necessary follow-up maintenance policy. When monitoring efforts spot an indicator, how easily can maintenance work be triggered and technicians sent out to service the asset?

If monitoring with spot testing

Ensure that the technician conducting the test can send their inspection details to the resolving technician (if it’s not the same individual). If the indicator is time-sensitive, make sure not to waste time in the hand-off.

If monitoring with sensor alerts

The easiest way to handle the hand-off is to integrate it with work order software. This way, the sensor can trigger a work order right away without human involvement. Ideally, it will include all the necessary details the technician needs to respond to the alert.

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