With the many advancements in Artificial Intelligence, Machine Learning and the advent of countless different sensors on the market, the question arises, “Is Oil Analysis still relevant today?”. Granted that these advancements have significantly transformed the industry, we need to recognize that they are here to help evolve what we already do and not necessary replace it.
These advancements build upon the foundations of the techniques of oil analysis. With artificial intelligence and machine learning, we can train models to interpret oil analysis data and trigger alerts accordingly but there should always be a human present to overview these. In the real world, not every situation has occurred or been recorded yet hence the models do not have that particular data to learn from nor can they make decisions about it since it simply doesn’t exist in their “brain”.
Humans can “think outside the box” and formulate patterns or trends which may not be triggered by the models simply because these models have not been taught these patterns. Hence it is important to always have a human in the loop and not rely solely on these models especially when million-dollar decisions can be negatively initiated with the wrong interpretations.
Lately, sensors have gained more traction and a wider adoption as they can be integrated into warning systems to alert users to potential deviation from known characteristics of the oil. However, as noted above, sensors rely on data sets to compare the information and on some form of capacitance which must be converted into a signal before it can be interpreted.
With lab equipment performing the actual tests, there is a higher rate of accuracy plus the added advantage of having humans review the results for discrepancies before sending off the report. While sensors can be the first warning system for some users, lab equipment should be utilized for those more precise tests which require a higher level of accuracy.
In essence, oil analysis remains very relevant today. However, it has significantly evolved over the last few decades. Today, oil analysis can achieve a higher efficiency level with the integration of the advancements in technology (AI, machine learning and sensors) and other available monitoring technologies. Together, these should all be used to create a greater impact on improving the reliability of the machines.
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