Fast and Noninvasive Determination of Viscosity of Lubricating Oil Based on Visible and Near Infrared Spectroscopy
Lecture Notes in Electrical EngineeringAdvances in Neural Network Research and Applications(2010)
Abstract
Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast and nondestructive determination of viscosity
of lubricating oil. A total of 150 oil samples were scanned, and different calibration models were developed with the pretreatment
of smoothing and standard normal variate. The input variables of calibration were the principal component selected by principal
component analysis (PCA) and characteristic wavelengths selected by successive projections algorithm (SPA). The calibration
model were developed using partial least squares (PLS), multiple linear regression (MLR) and back propagation neural networks
(BPNN). The results indicated that PCA-BPNN and SPA-BPNN models were better than the linear models (PCA-PLS, PCA-MLR, SPA-PLS
and SPA-MLR). The correlation coefficients were 0.971 for PCA-BPNN and 0.964 for SPA-BPNN. This demonstrated that BPNN could
make good use of the nonlinear information in spectral data, and SPA was a powerful way for the selection of characteristic
wavelengths. The selected wavelengths were helpful for the development of portable lubricating oil viscosity detection instrument.
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Key words
successive projections algorithm.,viscosity,visible and near infrared spectroscopy,lubricating oil,neural networks
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