The Four Types of Predictive Maintenance and Why They Matter

Predictive maintenance is a type of maintenance that uses data and analytics to predict when equipment will fail. This allows businesses to schedule maintenance before the equipment fails, which reduces downtime and the associated costs.

There are four main types of predictive maintenance: condition-based, time-based, usage-based, and model-based. In this article, we’ll explore the four types of predictive maintenance and their benefits.

Condition-based predictive maintenance

Condition-based predictive maintenance uses sensors to monitor the health of an asset. It predicts when a component will fail by measuring its health and performance over time. This type of predictive maintenance was developed for rotating equipment, such as turbines or motors.

For example, if a turbine rotor detects a blade crack, it can ramp up the rotation speed to reduce the load and avoid a catastrophic failure. Condition-based predictive maintenance is particularly useful for rotating equipment, such as turbines, motors, or compressors, because rotors take a long time to wear out.

Time-based predictive maintenance

Time-based predictive maintenance uses historical data to predict when a component will fail. This type of predictive maintenance is most effective when there is a clear pattern of failures. This type of maintenance is typically used for mechanical or electrical components.

For example, if an electricity meter shows that a machine is operating at half capacity, the machine can be programmed to shut down for maintenance before the motor fails. Time-based predictive maintenance is particularly useful for mechanical components that have a clear pattern of wear.

However, it’s less effective for rotating equipment, such as turbines or motors, because it monitors the health of the equipment and not its performance.

Usage-based predictive maintenance

Usage-based predictive maintenance relies on the amount of time an asset is used to predict when it will fail. This type of maintenance is most effective for assets that experience a lot of wear and tear or are prone to failure.

This type of maintenance is particularly useful for equipment that experiences extreme loads, such as cranes and forklifts. For example, if a crane lifts a heavy load and stays in that position for the entire shift, it can be programmed to shut down before the crane fails.

Usage-based predictive maintenance is less effective for rotating equipment, such as turbines or motors, because it monitors the amount of time an asset is used, not its performance.

Model-based predictive maintenance

Model-based predictive maintenance uses machine learning algorithms to predict when an asset will fail. This type of maintenance is often used with assets that have a high failure rate. This type of maintenance is particularly useful for assets that have a high failure rate, like electric motors.

For example, if a motor is operating at 50% capacity and has a high average failure rate, it can be programmed to shut down for maintenance before the motor fails.

Model-based predictive maintenance is less effective for rotating equipment, such as turbines or motors, because it monitors the average failure rate of an asset and not its performance.

The benefits of predictive maintenance

– Better utilization of assets – Predictive maintenance allows businesses to schedule maintenance before the equipment fails. This reduces downtime and the associated costs.

– Reduced risk of failure – Scheduling maintenance before the equipment fails reduces the risk of a catastrophic failure.

– Reduced maintenance costs – The equipment can be repaired before it fails, saving businesses the cost of replacing it.

Implementing predictive maintenance

Before implementing predictive maintenance, it’s important to understand the four types of predictive maintenance. Once you’ve identified which type of predictive maintenance is appropriate for your assets, it’s time to put the plan into action.

– Identify assets that need maintenance – First, you’ll need to identify the assets that need maintenance. This can be done manually or by using an asset management system.

– Monitor the assets – Next, you’ll need to monitor the assets to identify areas of concern. This can be done by manually inspecting the equipment or using sensors.

– Schedule maintenance based on the data – Once you’ve collected the data, you’ll be able to identify assets that are at risk of failing. You can then schedule the maintenance to fix the equipment before it fails.

– Monitor the results – Finally, you’ll want to monitor the results to identify areas of improvement. This will help you optimize your maintenance schedule in the future.

Conclusion

Predictive maintenance is a type of maintenance that uses data and analytics to predict when equipment will fail. This allows businesses to schedule maintenance before the equipment fails, which reduces downtime and the associated costs. There are four main types of predictive maintenance: condition-based, time-based, usage-based, and model-based.