Answered May 16 2019
Prescriptive maintenance, abbreviated as RxM, is a maintenance concept that collects and analyzes data about an equipment’s condition to come up with specialized recommendations and corresponding outcomes to reduce operational risks.
If you’ve heard of predictive maintenance, then you might find prescriptive maintenance as a strikingly similar concept. Predictive maintenance employs the use of sensors to precisely collect data describing an asset’s condition and overall operational state. The data can then be analyzed to predict when failure events will occur.
Prescriptive maintenance takes this analysis a notch higher by not only predicting failure events, but also recommending actions to take. Potential outcomes when such recommended actions are performed are then calculated and anticipated.
For example, given an equipment running with varying bearing temperature, predictive concepts will tell you when the equipment is likely to fail given its temperature profile. Prescriptive methods, on the other hand, tells you that reducing the equipment speed by a certain amount can double the time before it is likely to fail.
While predictive maintenance can tell you the estimated duration until a failure event, prescriptive maintenance will allow you to calculate the effects of varying the operating conditions to the time to failure.
As the capability to collect data increases, so does the complexity of analysis that can be done to interpret the information. Prescriptive maintenance is driven by what is known as prescriptive analytics. This type of analysis goes beyond predicting events, to exploring hypothetical outcomes. In effect, you can think of prescriptive analytics as a tool that provides you with multiple scenarios and simulations without having to experience each one in real life.
The idea is that through prescriptive analytics, algorithms and artificial intelligence assist maintenance teams to perform maintenance in a more exhaustive approach.
The future of prescriptive maintenance
The internet of things (IoT) and technological advancements are evidently accelerating our day-to-day activities and maintenance activities are no exception. Efforts to incorporate artificial intelligence and machine learning into maintenance programs are no longer just science fiction.
The possibilities for prescriptive maintenance are endless, and breakthroughs are being discovered each day. Being informed about such advancements is the first step to equip teams with innovative maintenance strategies.
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