光谱学与光谱分析 |
|
|
|
|
|
Research on Monitoring Mechanical Wear State Based on Oil Spectrum Multi-Dimensional Time Series Model |
XU Chao,ZHANG Pei-lin,REN Guo-quan,LI Bing,YANG Ning |
Department First,Ordnance Engineering College,Shijiazhuang 050003,China |
|
|
Abstract A new method using oil atomic spectrometric analysis technology to monitor the mechanical wear state was proposed. Multi-dimensional time series model of oil atomic spectrometric data of running-in period was treated as the standard model. Residues remained after new data were processed by the standard model. The residues variance matrix was selected as the features of the corresponding wear state. Then,high dimensional feature vectors were reduced through the principal component analysis and the first three principal components were extracted to represent the wear state. Euclidean distance was computed for feature vectors to classify the testing samples. Thus,the mechanical wear state was identified correctly. The wear state of a specified track vehicle engine was effectively identified,which verified the validity of the proposed method. Experimental results showed that introducing the multi-dimensional time series model to oil spectrometric analysis can fuse the spectrum data and improve the accuracy of monitoring mechanical wear state.
|
Received: 2009-12-28
Accepted: 2010-05-13
|
|
Corresponding Authors:
XU Chao
E-mail: xuchao198602@163.com
|
|
[1] ZHENG Chang-song,MA Biao,MA Yuan(郑长松,马 彪,马 源). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2009,29(3):749. [2] GAO Jing-wei,ZHANG Pei-lin,REN Guo-quan,et al(高经纬,张培林,任国全,等). Chinese Journal of Internal Combustion Engine Engineering(内燃机工程),2004,25(6):34. [3] ZHANG Ying-tang,LI Guo-zhang,REN Guo-quan(张英堂,李国璋,任国全). Lubrication Engineering(润滑与密封),2006, 31(12):10. [4] CAO Qing-wen,LI Ning,DONG Lian-wen(曹庆文,李 宁,董连文). High Voltage Engineering(高电压技术),2004,30(136):124. [5] ZHOU Yao,HONG Rong-jing,LI Lei,et al(周 尧,洪荣晶,李 磊,等). Machine Tool & Hydraulics(机床与液压),2007,35(6):228. [6] YANG Shu-zi,WU Ya,XUAN Jian-ping,et al(杨叔子,吴 雅,轩建平,等). Time Series Analysis Engeneering Application(Ⅱ),Second Edition(时间序列分析的工程应用(下册),第2版). Wuhan:Huazhong University of Science and Technology Publishing House(武汉:华中科技大学出版社),2007. 132. [7] ZHANG Yao-ting,FANG Kai-tai(张尧庭,方开泰). Introduction to Multivariate Statistical Analysis(多元统计分析引论). Beijing:Science Press(北京:科学出版社),1999. 322.
|
[1] |
CHEN Sheng, ZHANG Xun, XU Feng*. Study on Cell Wall Deconstruction of Pinus Massoniana during Dilute Acid Pretreatment with Confocal Raman Microscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2136-2142. |
[2] |
TAN Ai-ling1, WANG Si-yuan1, ZHAO Yong2, ZHOU Kun-peng1, LU Zhang-jian1. Research on Vinegar Brand Traceability Based on Three-Dimensional Fluorescence Spectra and Quaternion Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2163-2169. |
[3] |
WU Xiao-hong1, 2, ZHAI Yan-li1, WU Bin3, SUN Jun1, 2, DAI Chun-xia1,4. Classification of Tea Varieties Via FTIR Spectroscopy Based on Fuzzy Uncorrelated Discriminant C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1719-1723. |
[4] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
[5] |
WANG Chao, WANG Jian-ming, FENG Mei-chen, XIAO Lu-jie, SUN Hui, XIE Yong-kai, YANG Wu-de*. Hyperspectral Estimation on Growth Status of Winter Wheat by Using the Multivariate Statistical Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1520-1525. |
[6] |
WU Bin1, WANG Da-zhi2, WU Xiao-hong3, 4*, JIA Hong-wen1. Possibilistic Fuzzy K-Harmonic Means Clustering of Fourier Transform Infrared Spectra of Tea[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 745-749. |
[7] |
TU Yu-long, ZOU Bin*, JIANG Xiao-lu, TAO Chao, TANG Yu-qi, FENG Hui-hui. Hyperspectral Remote Sensing Based Modeling of Cu Content in Mining Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 575-581. |
[8] |
REN Yu1,2, SUN Xue-jian2*, DAI Xiao-ai1, CEN Yi2, TIAN Ya-ming1, WANG Nan2, ZHANG Li-fu2. Variety Identification of Bulk Commercial Coal Based on Full-Spectrum Spectroscopy Analytical Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 352-357. |
[9] |
WU Xi-yu1,2, ZHU Shi-ping1*, HUANG Hua1, XU Dan2, GUO Qi-gao3. Near Infrared Spectroscopy for Determination of the Geographical Origin of Huajiao[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 68-72. |
[10] |
TAN Nian1,SUN Yi-dan1,WANG Xue-shun1*,HUANG An-min2,XIE Bing-feng1. Research on Near Infrared Spectrum with Principal Component Analysis and Support Vector Machine for Timber Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3370-3374. |
[11] |
LI Chen-xi1, 2, SUN Zhe2, JIANG Jing-ying2*, LIU Rong1,2, CHEN Wen-liang1, 2, XU Ke-xin1,2. Typical Ground Object Recognition Based on Principle Component Analysis and Fuzzy Clustering with Near-Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3386-3390. |
[12] |
LIU Peng-xi, WAN Xiong*, ZHANG Ting-ting. Species Identify Based on Visible Absorb Spectrum of Whole Blood[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3510-3513. |
[13] |
WANG Hui-hui1,2, ZHANG Shi-lin1,2, LI Kai1,2, CHENG Sha-sha1, TAN Ming-qian1, TAO Xue-heng1,2, ZHANG Xu1,2*. Non-Destructive Detection of Ready-to-Eat Sea Cucumber Freshness Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3632-3640. |
[14] |
FANG Hui1, JIANG Lin-jun1, PAN Jian1, HE Yong1, GONG Ai-ping2, SHAO Yong-ni1*. Research on Microalgae Lipid Change under Nitrogen-Based Stress by Raman Microspectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(10): 3108-3111. |
[15] |
LIU Yan-de, HAN Ru-bing, ZHU Dan-ning, MA Kui-rong, XIAO Huai-chun, SUN Xu-dong. Nondestructive Testing for Yellow Peach Bruise and Soluble Solids Content by Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(10): 3175-3181. |
|
|
|
|