Chemometric methods applied to the calibration of a Vis–NIR sensor for gas engine's condition monitoring

This paper describes the calibration process of a Visible–Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil correlating transmittance oil spectra with the degradation of a gas engine's oil via a regression model. Chemometric techniques were applied to determine different parameters: Base Number (BN), Acid Number (AN), insolubles in pentane and viscosity at 40 °C. A Visible–Near Infrared (400–1100 nm) sensor developed in Tekniker research center was used to obtain the spectra of artificial and real gas engine oils.In order to improve sensor's data, different preprocessing methods such as smoothing by Saviztky–Golay, moving average with Multivariate Scatter Correction or Standard Normal Variate to eliminate the scatter effect were applied. A combination of these preprocessing methods was applied to each parameter. The regression models were developed by Partial Least Squares Regression (PLSR). In the end, it was shown that only some models were valid, fulfilling a set of quality requirements. The paper shows which models achieved the established validation requirements and which preprocessing methods perform better. A discussion follows regarding the potential improvement in the robustness of the models.

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► We describe the calibration process of a Visible–Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil.
► Chemometrics techniques were applied to determine Base Number (BN), Acid Number (AN), amount of insolubles in pentane and viscosity at 40 °C.
► In order to improve sensor data different preprocessing methods were applied taking into account both the oil parameters and sensor data.
► Although the results are promising, the models should be improved in order to decrease the prediction error.