Here you will find a number of short presentations and videos about vibration analysis and condition monitoring topics. Please take a look, we know you find them beneficial. Please note: The presentations not hosted on YouTube will require Adobe Flash® player to be installed on your PC, and is not available on most mobile devices.
How do make sure that everyone is on the same page - all pulling in the same direction to achieve improved reliability success? With merely "pockets of knowledge" your reliability initiative won't be sustainable. This iLearnReliability presentation examines this topic, and provides suggestions to avoid this common pitfall.
Everyone contributes to unreliability, therefore everyone must be trained to improve your plant's reliability. This overview presentation describes how you can take meaningful steps toward sustainable reliability improvement by subscribing to iLearnReliability. Plant-wide training for everyone at your plant from top management, reliability and condition monitoring program managers, skills training for the craftsmen and plant-wide awareness for everyone on the plant floor.
Reliability Improvement is achievable and sustainable without the need to hire expensive consultants who benefit by your program's dependence on them. Jason Tranter describes the Roadmap to Reliability Improvement that can be realized without outside help. Learn more at http://www.MobiusInstitute.com/reliability.
This 8 minute video by Jason Tranter gives you an overview
of the averaging process that occurs in the FFT analyzer. This video will
help you understand what averages are, why you take them and how
may you should take. Jason also describes where averaging occurs in
the data processing process. We think you will enjoy this video!
In this short video, Jason Tranter describes the
relationship between the lines of resolution of an FFT spectrum and the
number of averages one should select. This presentation is especially
relevant today because of the increased capacity of modern digital data
collectors. This increase in capacity has resulted in a different data
collection strategy than existed a few years ago.
In this 5 minute video, Jason Tranter describes the four
basic types of filters one finds in a vibration data collector. Filtering
is an essential part of vibration data collection and processing, whether
you know it or not, the data you are collecting is being filtered in a
number of ways. It is therefore important to have a general grasp of what
filters are and what they are used for in order to assure that data of
interest to you is not filtered out of the signal.
In every FFT vibration data collector, the user has to
configure four important data collector settings. In this 9 minute video,
Jason Tranter will explain these four important settings and will offer
some guidelines in how to configure your data collector. The four settings
- Fmax - describes the maximum frequency being measured for this test
- Lines of resolution - describes how much resolution the vibration
spectrum will have (similar to the resolution of a digital photo)
- Averages - The data collector will take a number of readings and
average them. How many do we need?
- Units - displacement, velocity or acceleration. Which are you
measuring and which do you want to analyze?
In this short video, Jason Tranter discusses averaging and
the FFT analyzer and answers the question: "How many averages should
I take?" Averaging in the FFT analyzer is used to reduce noise and to
get a better idea of what is happening in your machine over a short period
of time. In general, more averages will give you a longer time window in
which to view what is going on in the machine, on the down side, it will
take more time to collect the data. If one takes no averages or not enough
averages, the data collection time may be too short to give you a good
picture of what is happening with the machine. So, how do you select the
correct amount of averages? Watch this video and find out!
In this short presentation, Jason Tranter explains the use
of proximity probes on fluid film bearings or journal bearings and the
graphical display typically associated with this application; the orbit
plot. When monitoring a fluid film bearing with proximity probes (also
called displacement probes) one measures the distance between the shaft
surface and the inner surface of the bearing. Utilizing two probes
oriented 90 degrees from each other, one can create a plot that describes
the motion of the shaft within the bearing. This is the orbit. We
hope you enjoy this 8 minute video!
In this short video, Jason Tranter describes the concept of
overlap averaging in the FFT vibration analyzer. When collecting data with
a vibration data collector one typically uses averaging, which means that
a number of smples of vibration data are collected and averaged together
to provide a better view of what is happening in the machine being
monitored over a period of time. More averages may result in better
quality data, but it also takes more time to collect. Overlap averaging is
a way to make the averaging process more efficient such that one benefits
from having more averages but reduces the total amount of time required to
collect the data.
In this short presentation, Jason Tranter describes peak
hold averaging, what it is and how it works.
Peak hold averaging is not normally used for routine vibration
analysis, however it is a very useful took if you know that the vibration
will change during a test. The speed may change, the process may
change, you may perform a bump test, etc.
Peak hold averaging shows you how high the amplitude became at every
frequency during the test - as explained in this video.
In this short video, Jason Tranter explains the concept of
spectral resolution. When collecting data with an FFT analyzer, the user
must select how many lines of resolution to take. It is similar to taking
a photo with a digital camera and telling the camera how many mega pixels
you want to take. The general issue with resolution is that higher
resolution data provides more information (a clearer picture) but it takes
longer to collect and more room to store. Lower resolution data may be
collected faster and takes less room to store, but you may not be getting
the information you expect in the spectrum. So how do you decide how much
resolution is enough? Watch the video and find out!