The maintenance strategy comparison cheat sheet
This article will examine the differences between predictive maintenance and other widely used maintenance strategies.
What is predictive maintenance?
Predictive maintenance involves collecting data about the condition of an industrial asset, and using that data to predict when the asset will fail. That, in turn, enables you to fix or replace the asset before failure causes unplanned downtime.
The question is: why are maintenance professionals turning toward predictive maintenance and away from other maintenance strategies?
Predictive maintenance vs. corrective maintenance
Corrective maintenance is perhaps the most basic maintenance strategy: a fault occurs and the service engineer must then correct the fault. Very little data is collected or used. This strategy is often best suited to environments where there are only one or two assets running, and the cost of unplanned downtime is very low.
Compared to more modern maintenance strategies, corrective maintenance has a number of disadvantages. For example, in the event of a malfunction or if a part breaks, production will stop. If it takes a long time to source a new part or maintenance staff are not instantly available, that standstill could last for an extended period of time. For many industrial organizations, extended periods of downtime can severely damage productivity and profit.
Predictive maintenance is based on the assumption that unplanned downtime is not an option. A variety of asset health metrics are monitored, and as soon as one of them indicates a developing fault, the asset can be fixed or replaced well in advance, at a convenient time. That means no sudden equipment failures and no unplanned downtime.
Predictive maintenance vs. preventive maintenance
For some companies the cost of unplanned downtime is so high that they employ a preventive maintenance strategy. Here, inspections are carried out early and parts are replaced even before they show signs of a developing fault. This increases the equipment’s reliability and availability, and it can minimize the probability of unplanned downtime.
However, preventive maintenance often leads to unnecessary costs. Maintenance is scheduled early (often too early) to prevent assets from failing while in use. This in turn means assets are often replaced prematurely, which can substantially erode the machine’s return on investment and increase total cost of ownership.
Predictive maintenance + condition monitoring
As mentioned, a predictive maintenance strategy requires that the maintenance team can predict when a future fault is going to occur, and rectify the fault (either by fixing or replacing the asset) before the fault causes a failure and an unplanned downtime event.
The central tool enabling predictive maintenance is condition monitoring. There are a number of different ways to monitor the health of an industrial asset, and the method that works for you will depend on a number of factors including the type and number of assets you have. Popular methods of condition monitoring include vibration analysis, motor current signature analysis, oil analysis, acoustic analysis, thermal analysis and visual inspection. To read more about the different types of condition monitoring, read our comparison guide.
Our system, SAM4, uses motor current signature analysis to detect developing equipment faults up to 5 months in advance. To sign up for a SAM4 demo, click here.
Condition monitoring is also often easily scalable. Using automation and self-learning algorithms, data from hundreds of motors can be collected and analyzed, saving you from having to manually analyze huge amounts of data yourself. When a developing fault is detected, the system can quickly alert your maintenance team as to which asset is faulty, the reason the asset is faulty, and how long you have until the fault causes the asset to fail.
How could predictive maintenance benefit your operation?
To find out how predictive maintenance could benefit your organization, talk to one of our consultants or book a SAM4 demo.
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SAM4 is a predictive maintenance system that helps maximize asset uptime by detecting developing faults up to five months ahead. But there’s another plus: that same data provides concrete insights that enable energy and carbon reductions.
- White papers
In this report, we explain exactly how SAM4 detects common faults in hot strip mills using actual results from anonymized SAM4 data, to help steel engineers evaluate SAM4’s value for their own operations.
- Technical documents
- White papers