Identification of Transmission Fluid Based on NIR Spectroscopy by Combining Sparse Representation Method with Manifold Learning
JIANG Lu-lu1, LUO Mei-fu1, ZHANG Yu1，YU Xin-jie2, KONG Wen-wen3, LIU Fei3*
1. Zhejiang Technology Institute of Economy, Hangzhou 310018，China 2. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China 3. College of Biosystems Engineering and Food Science，Zhejiang University，Hangzhou 310058，China
Abstract：An identification method based on sparse representation (SR) combined with autoencoder network (AN) manifold learning was proposed for discriminating the varieties of transmission fluid by using near infrared (NIR) spectroscopy technology. NIR transmittance spectra from 600 to 1 800 nm were collected from 300 transmission fluid samples of five varieties (each variety consists of 60 samples). For each variety, 30 samples were randomly selected as training set (totally 150 samples), and the rest 30 ones as testing set (totally 150 samples). Autoencoder network manifold learning was applied to obtain the characteristic information in the 600~1 800 nm spectra and the number of characteristics was reduced to 10. Principal component analysis (PCA) was applied to extract several relevant variables to represent the useful information of spectral variables. All of the training samples made up a data dictionary of the sparse representation (SR). Then the transmission fluid variety identification problem was reduced to the problem as how to represent the testing samples from the data dictionary (training samples data). The identification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with that of linear discriminant analysis (LDA), least squares support vector machine (LS-SVM) and sparse representation (SR) using the relevant variables selected by principal component analysis (PCA) and AN. Experimental results demonstrated that the overall identification accuracy of the proposed method for the five transmission fluid varieties was 97.33% by AN-SR, which was significantly higher than that of LDA or LS-SVM. Therefore, the proposed method can provide a new effective method for identification of transmission fluid variety.
蒋璐璐1，骆美富1，张 瑜1，余心杰2，孔汶汶3，刘 飞3* . 汽车自动变速箱油的近红外光谱识别研究 [J]. 光谱学与光谱分析, 2014, 34(01): 64-68.
JIANG Lu-lu1, LUO Mei-fu1, ZHANG Yu1，YU Xin-jie2, KONG Wen-wen3, LIU Fei3* . Identification of Transmission Fluid Based on NIR Spectroscopy by Combining Sparse Representation Method with Manifold Learning. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(01): 64-68.
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