A computer program that taught itself the strategy game "Go" and beat the current world champion
Impressive advancements in facial recognition software Almost instantaneous translation of one language to another (either written or spoken) using freely available online software
With all these advancements, it is natural to ask whether such techniques could be effectively applied to lubrication problems.
The rise in these many examples of machine learning/artificial intelligence is directly linked to the availability of highquality data (and lots of it). Machine learning and artificial intelligence can generally be split into two types: supervised models and unsupervised models. Simply put, supervised models typically find statistical (or regression) models relating input data to output data and provided new input data that is covered by the training data set. These techniques should work well, although they are not able to reliably extrapolate if the new input data is outside that of the training set.
Unsupervised models typically use neural networks. Neural networks, also referred to as "deep learning", are a collection of algorithms loosely modeled on the human brain, designed to recognize patterns. These unsupervised models can effectively "work out" the best model for themselves however, once a good model has been found, it is not always obvious why it works. In other words, the underlying algorithm that the machine learning model has found is not easy to unravel. There are numerous examples where artificial intelligence and machine learning have already been applied within the lubrication/tribology community, including:
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Denne historien er fra July - August 2022-utgaven av MACHINERY LUBRICATION INDIA.
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