RESUMO
A method that could be used to quantify the water concentration in ship machinery lubricating oil based on Mid-infrared LED is discussed. A Mid-infrared LED with peak emission wavelength of 2 840 nm and FWHM of 400 nm is used as the light source, the emitting light is partly absorbed by the oil sample, the remaining is received by the infrared detector. The percentage of water is determined according to the absorbance. In the experiment, a optical configuration including the transmission, absorbing and receiving of infrared light is designed, calcium fluoride wafer is used as the window, a hard metal coil with circular section is selected as the washer to get the fixed thickness of oil film accurately, a photoelectric diode with detection wavelength of 2 500-4 800 nm and response time of 10-20 ns is used as the detector of light intensity. Matching with this, a system of signal preamplifier, microcontroller-based data acquisition, storage and communication is developed. Absorbance data of six oil samples with different water mass concentration: 0, 0.062 5%, 0.125%, 0.25%, 0.375% and 0.5% is acquired through experiment. Fitting the data by the method of least squares, a linear equation in terms of absorbance and water concentration is obtained, and the determination coefficient is 0.996. Finally, in order to test the accuracy of this measurement method, using oil sample with water concentration of 0.317 5% to validate the equation, measuring the absorbance by the experimental device, the water content is calculated through the linear equation, the results show that the relative error is 2.7% between the percentage calculated and the real sample, indicating that this method can accurately measure the water concentration in the oil.
RESUMO
In order to study wear characteristics of a 6-cylinder diesel engine, six different working statuses were arranged by altering the clearance between cylinder and piston. Sixty-nine oil samples were taken from engine at different loads under 6 working statuses and analyzed by Spectroil M Instrument made in US. Principal component analysis (PCA) was applied to analyzing spectrometric data of sixty-nine oil samples and clustering those data according to elements and oil samples separately based on the weighted coefficient and principal component scores. All 21 elements were used in element clustering and only 6 wear-related elements, namely iron, chromium, aluminum, copper, plumbum and silicon, were used in sample clustering. It is shown that PCA effectively clustered oil spectrometric data into three different principal components according to elements. The projection of two different principal components exhibited five types of elements combinations, namely wear elements (Fe, Cr, Cu, Al and Pb), high concentration additives elements (Na, Zn, P, Ca and Mg), low concentration additives elements (Ba and B), base constituent of lubricating oils (C and H) and interferential elements (Ni, Ti, Mo, V, Ag and Sn). Furthermore, PCA clearly clustered oil samples according to different clearance between cylinder and piston in the diesel engine. The study suggests that analyzing oil spectrographic data by PCA could find the sources of different elements, monitor engine conditions and diagnose wear faults.