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If you are like most people, you probably do not have alarm limits set up on your machines. You may have tried but you found that the volume of false alarms became so frustrating that you stopped running the exception report.

But imagine how things would change if you did have good alarms. You could unload the data collector and view a report of all the machines that you need to analyze because the vibration changed enough to warrant closer investigation. And you would also save a LOT of time by avoiding the analysis of all the remaining machines because you know the vibration barely changed.

How do you get to this state of bliss? Statistical alarms!

You should be aware that all machines vibrate in their own way. Not just because their condition is unique, but because of the "transmission path" between the bearing and the measurement point, and because of the way you mount the accelerometer, and because of the way the machine itself is mounted. That is the subject of a longer discussion, but let's just summarize by saying that in ALL machines, vibration at some frequencies is amplified, and at other frequencies it is reduced.

For that reason it is almost impossible to use "standards" to reliably set spectral alarm limits. Instead you have to learn from your machine what is normal and set your limits above that. And that is how statistical alarm generation works.

Just for a moment consider a single measurement point on a single machine. And now think about how the vibration amplitude has changed over the past six months at the running speed frequency (i.e. 1X) - assuming that the health of that machine has remained "normal" during that time.

We will consider two situations.

Vibration remains fairly constant:

If you could see that the vibration amplitude remained constant, varying less than 5%, then you would be surprised if the amplitude at 1X of the next measurement you collected was outside that normal range - 10% higher than the average for example. If it was within the 5% range you would consider it normal and would not need to look at the measurement, if it was significantly outside that range of "normal variation" then you would want to take a closer look at the spectrum.

Vibration varies a lot more:

Let's consider another situation. Over the past six months, while the machine was operating normally, the vibration at 1X was observed to vary by 20%. In each case you felt that the variation was normal and acceptable for that machine. As describe above, if the next measurement was within the normal range of variation, you would consider it normal and would not need to analyze the data. However if it was significantly outside that range then you would need to look at the measurement to check what was going on.

In very simple terms, that is how statistical alarms work. Instead of just setting the alarm at 1X, the software can repeat this calculation at all frequencies in every spectrum. The software can determine how much the vibration varies at every frequency, from every point, on every machine. You can tell the software how much variation you will tolerate before you should be warned. Most vibration analysis software packages can also automatically remove abnormal data from the calculations, set minimum and maximum thresholds, and compensate for speed variation.

The only challenge is to tell the software which measurements represent "normal" vibration for the machine. But with that investment in time you will save a huge amount of time every time you collect new measurements.

This method is proven, and it is supported by all modern software packages. It really is worth taking a closer look. To find out more write to learn@mobiusinstitute.com, call 877 550 3400, or click the link below to visit Mobius' website.

Tip provided by Mobius Institute

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