Mr. Ilia Yakimenko Profile

Ilia Yakimenko

at North-Caucasus Federal Univ

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Publications (1)

Proceedings Article | 6 July 2022 Paper

Oil conditioning sensors for online heavy-duty engine health monitoring based on the control of a limited number of parameters

Andrei Porokhnia, Ilia Yakimenko

Proceedings Volume 12296, 122960Q (2022) https://doi.org/10.1117/12.2643075

KEYWORDS: Sensors, Particles, Magnetism, Ultrasonics, Solids, Inductance, Diagnostics, Capacitance, Acoustics, Microfluidics

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The purpose of this article is to review new trends in monitoring the condition of engine oil on heavy-duty diesel engines. 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 replaces the old one. It changes the traditional routine preventive maintenance scheme and provides a deeper understanding of engine performance. Monitoring and checkout of the condition is necessary to maintain near real-time, because on-line control of equipment status can significantly reduce operating costs, by eliminating the need for equipment downtime 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. But there are some problems with this method. The first is the way of sampling. There are a lot of mistakes that can be made during the oil sampling process, and they can influence the results. Second is transportation to the place of analysis complicates the diagnostic process. That’s why we can’t say that this approach is an on-line method of diagnostic for the better prognosis of pending machinery failure needs to know a typical in time correlation between size and concentration of wear debris parts. But there is a note – a size of wear debris ranges in different work conditionals and depends on type of machine. For example, the size of wear debris during normal operation of a new engine ought to be at the range between 1 μm to 10 μm and at the constant correlation. If the conditions are abnormal the debris size will be between 20 μm and 100 μm. The size of wear debris parts and concentration of them will increase in time during the machine until it fails. The critical size is more than 100 μm the engine is in critical conditions and need to immediate stop and maintenance to avoid machine failure. That’s why to prevent emergency cutoff of the engine the debris size more than 20 μm needs to be controlled.

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