Answered May 03 2019
Almost by definition, predictive maintenance uses sensors, but the core principle of PdM doesn’t necessarily depend on them. There are two possible ways to incorporate PdM without using the usual sensors, and they’re kind of on opposite ends of the spectrum:
The first method requires you to be tracking of data already available to you. This data would include:
You’d need to pay very careful attention to the types of work done on assets and whether certain conditions seem to precipitate a failure. Timeframes may be important here—for instance, your data might suggest that the gasket on your hammer mill tends to fail every six months, or that the bearing system on a turbine tends to fail a month after it starts making that one grinding noise.
Probably the easiest way to track all this data is with the use of a CMMS. By using a CMMS to track maintenance reports, work orders, and other data related to a given asset, you can make predictions about when certain tasks should be performed to keep assets running.
Now, let’s take a look at the other method: finding ways to automatically monitor an asset’s condition without using traditional hardware.
One way to do this was recently showcased in Germany which used drivers with “virtual sensors.”
Essentially, what these drives do is measure the current, speed, and voltage on motors to infer data about the asset, such as oil temperature, vibration, or stator insulation. They found that the drive’s results weren’t far off from actual measurements, making this a possible alternative to regular sensors.
But again, it requires large volumes of data in order to work well.
In either case, in order to make PdM work without sensors, the one constant is you need to collect a great deal of information, figure out what it means, and set parameters based on your findings.
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