| 光谱学与光谱分析 |
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| Research and Evaluation on Wear in Power-Shift Steering Transmission Through Oil Spectral Analysis with RKPCA Method |
| LIU Yong1, 2, MA Biao1, ZHENG Chang-song1*, LI Shun-chang1，3 |
1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
2. School of Mechanical and Power Engineering, North University of China, Taiyuan 030051, China
3. CNPC Jichai Power Equipment Company, Ji’nan 250306, China |
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Abstract The most common methodology used in element concentration measurement and analyzing of wear particles is Atomic emission (AE) spectroscopy. The present paper presents an evaluation method on wear in power-shift steering transmission (PSST). By removing the problematic components which were highly correlated with oil additives, the robust kernel principal component analysis (RKPCA) method and the principal component analysis (PCA) method were accessed to extract the principal components of spectral data for oil samples collected from the life-cycle test of PSST in different stage and to calculate the amount of each principal component and its contribution rate respectively. A comparison between the above mentioned two methods was made to show that RKPCA method has fewer amounts of principal components and higher cumulative contribution rate indicating that RKPCA method acts more effectively in variable dimension reduction due to the outliers and nonlinearity of spectral data. Therefore, the effectiveness of RKPCA method in classification and identification of the wear in friction pairs was demonstrated subsequently through the correlation analysis between the variable coefficients of RKPCA and metal elements of friction pairs. The demonstration showed that RKPCA functioned precisely in the classification and identification of the wear in friction pairs, and in the evaluation on the wear in PSST. Thereafter, to detect the threshold point where the wear took place, the fuzzy C-means clustering algorithm was introduced to classify the RKPCA eigenvalues, and the results were compared with that of the spectral clustering algorithm. The fuzzy C-means clustering algorithm showed higher sensitivity in detecting the threshold point indicting a more precise evaluation on the wear in PSST. It is clear that the introduction of RKPCA method in wear evaluation, which takes the eigenvalues of spectral data as a critical variable to classify and identify the wear in different friction pairs as well as in the integral PSST configuration, shows better accuracy in wear prediction and will contribute to the reliable determination of life between overhauls and the accurate positioning of worn-out parts. As might be expected, the proposed method can be extended to other cases of wear detection and evaluation in complex mechanical system.
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Received: 2014-03-03
Accepted: 2014-06-15
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Corresponding Authors:
ZHENG Chang-song
E-mail: zhengchangsong@bit.edu.cn
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