What Is Condition-Based Monitoring?

An engineer observing the performance of an industrial machine using I-IoT technology installed on a tablet

You rely on your assets, and you need to know if they are at risk of failure or degradation. Even the most well-maintained machines eventually break down. Unpredictable failure leads to expensive repairs, lost productivity, and even safety-related hazards.

Before condition-based monitoring and predictive maintenance were feasible, companies estimated failure rates based on historical data. They knew, for instance, that a given asset might perform for 20,000 hours, so they planned for downtime between 15,000 and 25,000 hours. They had to manage their assets reactively and prepare for disruption.

Today, we have better technology like sensors, 5G, and predictive maintenance suites. We can determine with great confidence not only when a machine might fail but when a machine requires proactive maintenance. These proactive machine maintenance strategies are based on sensor-collected data and condition-based monitoring.

What is Condition-Based Monitoring?

Condition-based monitoring is a type of maintenance that uses machine-level sensors to monitor specific metrics, such as heat and vibration. The data collected by these sensors is then used to predict when a machine will likely fail so that maintenance can be carried out before the failure occurs.

The Rise of Condition-Based Monitoring

The shift from reactive to proactive maintenance methods was spurred by the Industrial Internet of Things (I-IoT). Better connected sensors enable machines to communicate complex, comprehensive data about their status in real time. Predictive maintenance uses the data collected by these sensors to predict when the asset needs maintenance and when it’s likely to fail.

Predictive maintenance is driven by condition-based monitoring. It is a preventative approach to maintenance that can be used to avoid unplanned downtime and reduce the cost of repairs. It is an essential tool for managing the increasing complexity of modern machinery, especially in an era in which companies may need to maintain an inventory of multiple asset models in varying phases of their lifecycle.

Still, a perfect storm of factors had to come together to make condition-based monitoring truly viable. 5G internet lowered latency across the IoT, sensors became more robust (and longer-lasting), and artificial intelligence (AI)/machine learning algorithms were developed to mine deeper into data to provide increasingly accurate predictions.

The P-F Curve in Condition-Based Monitoring

The P-F curve is a graphical representation of the relationship between the probability of failure and time to failure. As time progresses, the condition of the asset degrades. As the asset’s condition degrades, maintenance becomes predictive, proactive, or reactive.

A graphic showing the P-F curve, with condition as the vertical dimension and time as the horizontal dimension. The curve starts high and ends low, moving from predictive maintenance to proactive maintenance and finally reactive maintenance.

On the P-F curve, there are different stages of maintenance, which are:

  • Condition-based monitoring addresses the potential for failure while the asset is still operational. 
  • Proactive/preventative maintenance is completed while the asset is in the zone of potential failure. 
  • Reactive maintenance takes place after failure has already occurred.

The P-F interval is the time between the onset of a fault and the point at which the probability of failure reaches 1.0—in other words, the point at which failure is a certainty. This interval represents the window of opportunity for repair or replacement.

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Condition-Based Monitoring vs. Predictive Maintenance

Predictive maintenance is a type of condition-based monitoring that uses data collected by sensors to predict when a machine is likely to fail. Otherwise, condition-based monitoring can be used for anything real-time sensors can—including managing on-site safety or production and efficiency levels.

Important Equipment Depreciation Terms

Cost of Asset

The initial purchase price of the equipment.

Salvage Value

The estimated value of the equipment at the end of its useful life.

Useful Life

The estimated period over which the asset is expected to be used before it is fully depreciated.

Annual Depreciation

The amount of depreciation expense allocated for each year.

Book Value

The value of an asset in the business books, considering its cost minus accumulated depreciation.

Depreciable Base

The total amount that can be depreciated over the asset’s life is calculated as the Cost of the Asset minus the Salvage Value.

Units of Production

The total units produced or hours used during a year.

The Benefits of Condition-Based Monitoring

Condition-based monitoring is a form of predictive maintenance. When applied, it improves the predictability of failure rates and improves ROI. There are many benefits to using condition-based monitoring, including:

Improved machine uptime

Condition-based monitoring can predict when a machine will likely fail, so maintenance can be carried out before the failure happens, improving machine uptime.

Reduced maintenance costs

In addition to avoiding unplanned downtime (and last-minute purchases), companies can forecast their need for spare parts and purchase them when economically feasible (rather than just-in-time).

Improved safety

Sensors can detect the exact type and point of failure, preparing maintenance technicians for the asset’s status and thereby increasing the safety and predictability of repairs.

Increased productivity

Employees, and operations processes, will be able to work continuously rather than deal with disruption.

Condition-based monitoring isn’t just an effective approach to machine maintenance—it’s the future of machine maintenance. Presently, what’s holding most organizations back is the inability to orchestrate a broad shift toward an intelligent I-IoT facility. LLumin can help.

Intelligent, Condition-Based Monitoring With LLumin

LLumin’s predictive maintenance platform is a complete solution for condition-based monitoring. Not only does LLumin’s CMMS+ monitor real-time, machine-level data, but it also analyzes it based on historical information and machine learning algorithms. Using CMMS+, maintenance technicians can stay ahead of machine failures and avoid unplanned downtime with real-time insights into machine status.

Along with the CMMS+ software, you get top-notch customer service by your side throughout the entire process. We provide a complete implementation plan and team to help, quickly, accelerate your digital transformation.

Getting Started With LLumin

LLumin develops innovative CMMS software to manage and track assets for industrial plants, municipalities, utilities, fleets, and facilities. If you’d like to learn more about the total effective equipment performance KPI, we encourage you to schedule a free demo or contact the experts at LLumin to see how our CMMS+ software can help you reach maximum productivity and efficiency goals.

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