Bearings are one of the critical components in rotary machines such as motors, wind turbines, helicopters, automobiles, and gearboxes. Most of these machine failures are caused by bearing faults. Thus, being able to detect bearing faults and predict remaining useful life (RUL) can help to provide advance failure warnings, plan the maintenance schedule, and avoid catastrophic failures. Based on this, what are the most prevalent methods for bearing degradation detection and remaining useful life prediction?
Unambiguous description of technical condition is the key. If You are interested You can read my article:
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There is a screenshot of software that I programmed in order to detect material loss on cylindrical shaped objects - OD. This solution to gather many diagnostics data. Could be also used a tool similar to mine... Additionally the data like this could be used to train AI in order to determine wear process. Because AI is second best metod it is also possible to write algorithm based on data mining that will predict worn of bearings with similar environment and load conditions. Bearings wear progression for ex. spalling which is WMF[according to article] subclass of mechanical-fatigue worn, could be measured with 3D scanning and described as material loss in [g].
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