润滑油冷却液污染的拉曼光谱检测方法研究

doi:10.3964/j.issn.1000-0593(2021)03-0817-05

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Abstract For marine diesel engines, lubricating oil is often contaminated by the coolant, resulting in the deterioration of lubricating oil, further leading to its functional failure. The main components of the coolant are water, ethylene glycol, and a small number of additives such as anti-corrosion, anti-cavitation, and defoaming. The application of Raman spectrum to detect the concentration of coolant contaminating lubricating oil is a kind of Raman spectrum detection problem for complex mixtures. The quantitative analysis method of single Raman peak strength cannot meet the quantitative detection of concentration. Therefore, Raman spectral analysis and LSTM neural network data mining are applied to lubricant coolant contamination. Under laboratory conditions, diesel oil samples with coolant contamination concentrations of 2%, 1.5%, 1%, 0.5%, 0.25% and 0% were prepared. Each oil sample was analyzed by Raman spectroscopy for 50 times, and a total of 300 Raman spectral data were obtained. 80% of the data were randomly selected as neural network training samples, and the remaining data were taken as test samples. The wavenumber of Raman spectral sample data was 300～2 000 cm^-1. Data preprocessing, including sampling, fitting, discrete point average gradient estimation. The training sample set was constructed, and the LSTM neural network was combined with multi-layer full connection layer (FC) to establish four different neural network model structures, including FCs, LSTM-FCs-1, LSTM-FCs-2, andLSTM-FCs-3. The average error curves and detection accuracy curves of the four networks on the training set and test set are obtained. The results showed that the accuracy of FCs, LSTM-FCs-1, LSTM-FCs-2, and LSTM-FCs-3 neural network models was 96.7%, 93.3%, 98.3% and 83.3%, respectively. In order to study the robustness of the four models, the detection accuracy of the four neural network models was analyzed by selecting any wavenumber of 1% and adding noise whose amplitude changed by 1% randomly. The results were 88.3%, 90.0%, 96.7% and 78.3%, respectively. It can be seen that compared with the other three neural network structural models, LSTM-FCs-2 model is more suitable for quantitative estimation of lubricant coolant contamination, and its highest accuracy can still reach 96.7% after adding noise, and its robustness is better than the other three models. Raman spectroscopy combined with the LSTM-FCs-2 model in the LSTM network was applied to the sample of lubricating oil in use with 0.2% and 0.4% coolant contamination concentrations, respectively, with relative errors of 5.0% and 7.5%. It shows that this method can be used to detect the concentration of used lubricating oil contaminated by the coolant.

Key words：Raman spectroscopy; Diesel engine lubricating oil; Neural network; Quantitative estimates; Coolant contamination

Received: 2020-02-08 Accepted: 2020-06-19

ZTFLH:

O657.3

Corresponding Authors: MING Ting-feng E-mail: hxtianwuhan@aliyun.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)03-0817-05 OR https://www.gpxygpfx.com/EN/Y2021/V41/I03/817