• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 8, 2631 (2022)
Jiang ZHANG1,*, Jun-jie CUI1,1;, Chang-song ZHENG2,2; *;, Yong LIU1,1; *;..., Ya-jun LIU3,3; and Jian SHEN1,1;|Show fewer author(s)
Author Affiliations
  • 11. College of Mechatronics Engineering, North University of China, Taiyuan 030051, China
  • 22. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
  • 33. Inner Mongolia North Heavy Industry Group Co., Ltd., Baotou 014000, China
  • show less
    DOI: 10.3964/j.issn.1000-0593(2022)08-2631-06 Cite this Article
    Jiang ZHANG, Jun-jie CUI, Chang-song ZHENG, Yong LIU, Ya-jun LIU, Jian SHEN. Stochastic Process Prediction of Clutch Remaining Life Based on Oil Spectral Data[J]. Spectroscopy and Spectral Analysis, 2022, 42(8): 2631 Copy Citation Text show less
    Structural principle diagram of integrated transmission device and wet clutch
    Fig. 1. Structural principle diagram of integrated transmission device and wet clutch
    Physical drawing of wet clutch comprehensive test bench
    Fig. 2. Physical drawing of wet clutch comprehensive test bench
    Experimental flow chart
    Fig. 3. Experimental flow chart
    Elemental spectrum
    Fig. 4. Elemental spectrum
    Accumulation diagram of Cu and Pb elements
    Fig. 5. Accumulation diagram of Cu and Pb elements
    Degradation increment diagram(a): Cu increment diagram; (b): Pb increment diagram
    Fig. 6. Degradation increment diagram
    (a): Cu increment diagram; (b): Pb increment diagram
    Histogram of normal distribution of incremental statistics(a): Cu increment statistics chart; (b): Pb increment statistics chart
    Fig. 7. Histogram of normal distribution of incremental statistics
    (a): Cu increment statistics chart; (b): Pb increment statistics chart
    Prediction diagram of univariate wiener process
    Fig. 8. Prediction diagram of univariate wiener process
    Prediction diagram of binary wiener process
    Fig. 9. Prediction diagram of binary wiener process
    Residual life prediction deviation
    Fig. 10. Residual life prediction deviation
    Copula分布函数表达式
    FrankC(u,v)=-1αln1+[exp(-αu)-1][exp(-αv)-1]exp(-α)-1
    CaucasianC(u,v)=msubsupmsubsup12π1-α2·exp-x2-2αxy+y22(1-α2)dxdy
    GumbelC(u,v)=exp(-((-lnu)α+(-lnv)α)1α)
    ClaytonC(u,v)=max((u-α+v-α-1)-1α,0)
    Table 1. Common copula functions
    CopulaFrankCaucasianGumbelClayton
    AIC-19.22-11.42-7.36-8.24
    Table 2. AIC values of common copula functions
    模型η1η2σ1σ2α
    300.548 148 1480.496 296 2960.649 80.953 50.842
    600.291 228 0700.228 070 1750.904 01.378 80.839
    900.298 850 5750.232 183 9081.241 21.681 60.822
    1200.261 538 4620.229 914 5301.387 72.008 10.847
    1500.219 727 8910.159 863 9461.608 52.226 20.854
    1800.175 706 2150.206 779 6611.969 62.172 80.874
    2100.147 342 9950.138 164 2512.014 02.440 50.822
    2400.158 649 7890.127 426 1601.953 22.480 80.814
    Table 3. Estimated value of model coefficient
    预测时刻
    /h
    试验值一元模型二元模型
    预测/h偏差/%预测/h偏差/%
    3024516532.718823.3
    6021512044.213139.0
    901858752.911736.8
    1201557849.711029.0
    1501256746.49920.8
    180956035.27718.9
    210655712.3616.2
    240353911.44117.1
    Table 4. Comparison between prediction results and test results
    Jiang ZHANG, Jun-jie CUI, Chang-song ZHENG, Yong LIU, Ya-jun LIU, Jian SHEN. Stochastic Process Prediction of Clutch Remaining Life Based on Oil Spectral Data[J]. Spectroscopy and Spectral Analysis, 2022, 42(8): 2631
    Download Citation