| Summary: | The purpose of this article is to review new trends in monitoring the condition of oil on all factory area processes. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies, which are applicable for data-based maintenance. They are called predictive maintenance. This paradigm is going to replace the old one. It changes the traditional routine preventive maintenance scheme and provides a deep understanding of the equipment performance [1]. Monitoring and checkout of conditions are necessary to maintain in a real-time environment because on-line control of equipment status can put down an operating cost, by eliminating the need for equipment outage for everyday diagnostics. The analysis based on oil samples is an effective tribotechnical systems approach for early diagnosis of failures, as it contains valuable information about the process of degradation of oil and the state of tribotechnical pairs [2]. But there are some problems with this method. The first is the way of oil sampling. There are lots of mistakes that may be made during the oil sampling process, and they can affect the results. The second is a delivery to laboratory which complicates the diagnostic process. That’s why we cannot say this approach is an on-line method of diagnostics. For the better prognosis of pending machinery failure one needs to know a real-time correlation between size, shapes, and concentration of wear debris parts [3].
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