Improvement of condition-based monitoring for large bore 2-stroke marine engine cylinder liners and piston rings require close monitoring of anomaly wear elements in drain cylinder oil. This paper presents monitoring of seagoing ship engines because the environment of sailing affects the internal combustion conditions leading to mechanical failure of the cylinder liners. We presented condition-based monitoring of cylinder liners using X-ray to analyze drain cylinder oil samples. The approach for condition-based monitoring uses X-ray results to estimate wear amount and machine learning algorithm on elements in the drain cylinder oil samples. The wear amount is quantified for the purpose to estimate the degradation of cylinder liner materials through the drain cylinder oil samples, machine learning algorithm to evaluate the correlation of detected elements in drain oil samples. Finally, wear rate estimation to know the remaining useful life of the cylinder liner. Our results contributed to the improvement of condition-based monitoring of cylinder liners wear elements by quantification and machine learning to correlate elements in oil samples. Correlating wear elements facilitate quick decision-making on maintenance policies for slow-speed large bore 2-stroke engines.
Sulfur and phosphorus additives are used in lubricants as extreme-pressure and antiwear agents, which are typically used together to ensure reliability over a wide range of lubrication conditions. However, the working mechanism of the combined additive system has not been clearly defined due to difficulties obtaining information on the material surface where these additives work. This is because this surface is constantly being worn during testing. Therefore, in situ analysis applying an acoustic emission (AE) technique was proposed. AEs are elastic stress waves generated during the deformation and fracture of solids, which can be measured in real-time, providing information with respect to the magnitudes and types of damage. In this paper, an application of the AE helps to clarify how each additive acts on the surface in real-time. The working mechanism to understand improved reliability using both sulfur and phosphorous additives was investigated by the AE technique, along with conventional surface analysis methods. It is concluded that wear reducing properties were improved by the reaction of sulfur additives to remove the protruded parts, followed by the reaction of phosphorous additives to form a protective antiwear film.
The purpose of this study is to identify tribological phenomena that occur on a machined surface under grinding, using acoustic emission (AE) sensing. The study investigated features exhibited by AE signals due to a change of state of the machined surface during the finish grinding of glass materials. An AE sensor was attached to a glass test piece, and the AE signals generated by using a grinding wheel rotating at high speed were measured. It was found that differences in the grain size of the grinding wheel and the hardness of the test piece changed the amplitude of the AE signal, and that there was a correlation between the amount removed by grinding and the AE mean value. In addition, a frequency analysis of the AE signal waveforms revealed that the AE frequency components generated during friction and during grinding differ. It was also found that a change in grinding ability of the grinding wheel, due to it wearing flat or to abrasive grains falling away, can be established from the change in AE mean value and the peak position in the AE frequency spectrum.