Time Waveforms give us a summary of all of the vibration that it records during the time that it is recording. It tells us exactly what happens from moment to moment. Jason Tranter will explain more in detail what happens based on the information that is recorded.
As a vibration analyst, your ability to detect the early signs of rolling element bearing defects and track them as the fault progresses is key to your success. Unexpected failures should never happen, and late warnings will not be appreciated by the maintenance or production departments.
Have you ever wondered exactly how machine learning works? Where does the system get the data for equipment that is not allowed to fail often? Jason Tranter goes into a method an AI system might use to tell us whether a bearing is about to fail…
Do you know the difference between AI, machine learning, predictive analytics, data analytics, IIoT, and predictive maintenance? Jason Tranter goes into the definitions with examples…
When making a business case for predictive maintenance, you must first ask what management want. You should then ask what the employees want. Jason Tranter explains…
This webinar will help to explain why it is so important to perform time waveform analysis, and it will describe the types of patterns you will see in the waveform, and how to properly measure the waveform.
There are four basic types of machine learning, based on the kind of information they give you and how the machine “learns”: supervised, unsupervised, semi-supervised, and reinforcement. Jason Tranter defines these and gives an example…
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