系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (5): 1420-1429.doi: 10.12305/j.issn.1001-506X.2021.05.32
收稿日期:
2020-02-24
出版日期:
2021-05-01
发布日期:
2021-04-27
通讯作者:
汪建均
E-mail:jjwang@njust.edu.cn;1048298370@qq.com;317321361@qq.com
作者简介:
汪建均 (1977—), 男, 副教授, 博士研究生导师, 博士, 主要研究方向为质量管理与质量工程、工业工程、应用统计学。E-mail: 基金资助:
Jianjun WANG*(), Guikang YANG(), Zebiao FENG()
Received:
2020-02-24
Online:
2021-05-01
Published:
2021-04-27
Contact:
Jianjun WANG
E-mail:jjwang@njust.edu.cn;1048298370@qq.com;317321361@qq.com
摘要:
在加速寿命试验的可靠性设计中, 随机化设计的限制以及删失数据不可避免地导致低分位数估计出现较大的偏差。针对上述的问题, 结合贝叶斯抽样技术以及非线性混合模型(nonlinear mixed model, NLMM)提出了一种可靠性改进的分析方法。首先, 需要检验所收集的数据是否服从威布尔分布以及验证形状参数是否是恒定常数。其次, 考虑随机效应对尺度参数和形状参数的影响, 运用NLMM构建了尺度参数和形状参数与试验因子之间的函数关系。然后, 利用贝叶斯方法估计低分位数的可靠性寿命。最后, 实际案例研究表明, 在考虑删失问题和未完全随机设计的影响时, 所提方法能够获得更为稳健和可靠的估计结果。
中图分类号:
汪建均, 杨桂康, 冯泽彪. 加速寿命数据的贝叶斯建模与分析[J]. 系统工程与电子技术, 2021, 43(5): 1420-1429.
Jianjun WANG, Guikang YANG, Zebiao FENG. Bayesian modeling and analysis of accelerated life data[J]. Systems Engineering and Electronics, 2021, 43(5): 1420-1429.
表2
试验设计及寿命数据"
试验次序 | 温度/℃ | 电压/V | 寿命数据 | |||||||
1 | 170 | 200 | 439 | 904 | 1 092 | 1 105 | 1 105* | 1 105* | 1 105* | 1 105* |
2 | 170 | 250 | 572 | 690 | 904 | 1 090 | 1 090* | 1 090* | 1 090* | 1 090* |
3 | 170 | 300 | 315 | 315 | 439 | 628 | 628* | 628* | 628* | 628* |
4 | 170 | 350 | 258 | 258 | 347 | 588 | 588* | 588* | 588* | 588* |
5 | 180 | 200 | 959 | 1 065 | 1 065 | 1 087 | 1 087* | 1 087* | 1 087* | 1 087* |
6 | 180 | 250 | 216 | 315 | 455 | 473 | 473* | 473* | 473* | 473* |
7 | 180 | 300 | 241 | 315 | 332 | 380 | 380* | 380* | 380* | 380* |
8 | 180 | 350 | 241 | 241 | 435 | 455 | 455* | 455* | 455* | 455* |
表3
模型Ⅰ~Ⅴ的参数估计"
参数 | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
θ0 | 13.407 0 | 13.519 70 | 13.216 3 | 15.646 50 | 15.000 0 |
θ1 | -0.028 9 | -0.029 60 | -0.028 0 | -0.043 20 | -0.039 5 |
θ2 | -0.005 9 | -0.005 90 | -0.005 8 | -0.005 20 | -0.005 1 |
γ0 | - | - | 0.419 7 | -5.700 70 | -12.190 0 |
γ1 | - | - | 0.008 2 | 0.047 20 | 0.110 5 |
γ2 | - | - | -0.003 1 | -0.004 90 | -0.013 1 |
σμ | - | 0.047 46 | - | 0.184 14 | 0.262 7 |
σε | - | - | - | - | 0.400 8 |
表4
模型Ⅰ~Ⅴ的尺度参数和形状参数估计"
试验次序 | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ | |||||||||
尺度参数 | 形状参数 | 尺度参数 | 形状参数 | 尺度参数 | 形状参数 | 尺度参数 | 形状参数 | 尺度参数 | 形状参数 | |||||
1 | 1 496.77 | 2.75 | 1 492.86 | 2.78 | 1 470.850 | 3.37 | 1 432.060 | 3.80 | 1 397.0 | 3.77 | ||||
2 | 1 113.79 | 2.75 | 1 112.08 | 2.78 | 1 100.570 | 2.89 | 1 104.550 | 2.98 | 1 244.0 | 3.23 | ||||
3 | 828.80 | 2.75 | 828.43 | 2.78 | 823.510 | 2.48 | 851.940 | 2.33 | 828.3 | 2.56 | ||||
4 | 616.73 | 2.75 | 617.13 | 2.78 | 616.195 | 2.13 | 657.105 | 1.82 | 723.0 | 2.01 | ||||
5 | 1 121.00 | 2.75 | 1 110.27 | 2.78 | 1 111.480 | 3.66 | 929.870 | 6.10 | 1 198.0 | 5.32 | ||||
6 | 834.20 | 2.75 | 827.08 | 2.78 | 831.670 | 3.14 | 717.210 | 4.77 | 608.8 | 4.30 | ||||
7 | 620.75 | 2.75 | 616.12 | 2.78 | 622.300 | 2.70 | 553.189 | 3.73 | 488.6 | 3.87 | ||||
8 | 461.92 | 2.75 | 458.97 | 2.78 | 465.640 | 2.31 | 426.676 | 2.92 | 503.5 | 3.27 |
表5
模型Ⅰ~Ⅴ的分位数估计以及置信区间"
次序 | 模型 | t.01 | t.05 | t.1 | t.5 |
Ⅰ | 280.76[179.5, 475.3] | 508.00[367.1, 728.4] | 660.08[498.4, 882.5] | 1 309.92[1 026.5, 1 576.8] | |
Ⅱ | 284.51[183.2, 516.9] | 511.90[367.7, 768.7] | 663.50[491.0, 926.4] | 1 308.16[972.5, 1 625.1] | |
1 | Ⅲ | 375.80[200.1, 675.40] | 609.45[405.9, 882.8] | 754.52[550.8, 1 003.7] | 1 319.33[1 080.2, 1 531.2] |
Ⅳ | 427.31[243.2, 779.3] | 655.91[452.0, 965.4] | 792.55[589.8, 1 067.3] | 1 300.51[1 081.9, 1 492.2] | |
Ⅴ | 381.80[119.9, 635.9] | 591.50[287.4, 859.5] | 722.60[416.9, 994.9] | 1 250.00[951.3, 1 703.0] | |
Ⅰ | 208.92[137.1, 344.4] | 378.02[285.5, 520.7] | 491.18[389.9, 629.3] | 974.750[821.5, 1 100.1] | |
Ⅱ | 211.94[142.2, 372.9] | 381.33[284.3, 550.1] | 494.26[383.8, 659.9] | 974.490[772.0, 1 138.9] | |
2 | Ⅲ | 224.37[149.5, 374.1] | 394.17[302.7, 544.1] | 505.54[408.7, 645.6] | 969.600[840.4, 1 074.5] |
Ⅳ | 235.33[158.5, 420.6] | 407.01[310.8, 587.7] | 518.42[417.0, 682.6] | 976.520[835.1, 1 098.4] | |
Ⅴ | 288.00[91.98, 487.9] | 475.50[234.6, 706.3] | 596.60[347.7, 843.8] | 1 101.00[833.9, 1 475.0] | |
Ⅰ | 155.46[102.3, 258.1] | 281.29[211.9, 389.1] | 365.50[289.9, 470.4] | 725.340[603.9, 829.80] | |
Ⅱ | 157.88[106.1, 275.5] | 284.07[213.7, 407.7] | 368.19[287.7, 488.5] | 725.930[576.9, 847.60] | |
3 | Ⅲ | 129.05[73.20, 239.5] | 248.86[171.6, 368.1] | 332.59[248.6, 449.2] | 710.460[582.4, 826.90] |
Ⅳ | 117.99[66.60, 249.3] | 237.72[163.6, 379.7] | 323.90[242.1, 459.4] | 727.790[594.2, 846.80] | |
Ⅴ | 131.80[33.08, 238.7] | 246.30[104.9, 378.9] | 327.20[168.6, 481.1] | 708.500[493.8, 997.90] | |
Ⅰ | 115.68[74.20, 197.6] | 209.32[150.7, 302.8] | 271.98[203.7, 366.0] | 539.740[416.0, 666.00] | |
Ⅱ | 117.61[77.10, 211.6] | 211.61[152.6, 317.2] | 274.28[203.9, 382.3] | 540.770[403.4, 668.30] | |
4 | Ⅲ | 71.060[19.20, 190.3] | 152.77[66.50, 285.3] | 214.20[113.5, 345.9] | 518.770[381.6, 660.30] |
Ⅳ | 52.470[11.80, 181.9] | 128.49[49.30, 278.2] | 190.83[92.50, 341.3] | 537.250[400.6, 664.50] | |
Ⅴ | 72.860[2.948, 183.1] | 152.80[19.28, 304.5] | 216.20[49.85, 387.1] | 584.000[371.1, 925.40] | |
Ⅰ | 210.28[132.6, 362.9] | 380.48[270.6, 553.1] | 494.38[367.4, 674.3] | 981.100[750.7, 1 210.8] | |
Ⅱ | 211.60[135.6, 382.9] | 380.71[271.1, 572.8] | 493.46[363.7, 687.2] | 972.910[723.7, 1 208.5] | |
5 | Ⅲ | 316.52[153.4, 571.1] | 493.95[311.5, 723.4] | 601.23[421.2, 809.3] | 1 005.63[798.2, 1 202.1] |
Ⅳ | 437.26[284.6, 654.3] | 571.27[431.2, 745.7] | 642.87[514.2, 794.1] | 875.620[730.3, 997.60] | |
Ⅴ | 481.30[203.9, 713.6] | 658.80[373.1, 877.8] | 759.00[483.1, 961.8] | 1 110.00[923.6, 1 353.0] | |
Ⅰ | 156.48[102.4, 261.0] | 283.13[211.3, 394.2] | 367.89[289.6, 475.7] | 730.060[605.7, 836.80] | |
Ⅱ | 157.63[104.6, 277.4] | 283.61[210.3, 409.4] | 367.59[284.7, 490.6] | 724.750[574.3, 846.80] | |
6 | Ⅲ | 192.40[122.1, 325.4] | 323.19[239.6, 449.7] | 406.39[320.9, 522.5] | 740.110[633.5, 827.00] |
Ⅳ | 273.33[203.4, 404.7] | 384.71[316.7, 4912.0] | 447.40[382.0, 539.1] | 664.150[582.0, 723.10] | |
Ⅴ | 194.60[87.61, 283.3] | 287.70[174.5, 388.3] | 343.30[235.7, 453.1] | 553.300[424.6, 798.40] | |
Ⅰ | 116.44[76.80, 193.3] | 210.68[159.8, 290.7] | 273.75[218.2, 351.1] | 543.260[454.8, 615.10] | |
Ⅱ | 117.42[78.50, 205.3] | 211.27[157.0, 304.0] | 273.83[213.6, 365.2] | 539.890[427.0, 630.20] | |
7 | Ⅲ | 113.00[70.70, 196.0] | 206.82[151.5, 293.9] | 270.11[211.2, 353.7] | 543.210[453.8, 622.80] |
Ⅳ | 161.14[117.7, 251.7] | 249.46[200.1, 331.0] | 302.57[251.9, 376.4] | 501.410[422.2, 565.50] | |
Ⅴ | 139.70[58.35, 215.1] | 214.60[127.6, 292.4] | 260.60[172.0, 343.6] | 440.000[334.6, 598.90] | |
Ⅰ | 86.640[56.00, 147.0] | 156.77[114.6, 224.4] | 203.71[154.6, 271.2] | 404.260[317.0, 489.10] | |
Ⅱ | 87.470[57.30, 157.5] | 157.38[113.6, 235.9] | 203.99[151.6, 283.7] | 402.190[298.4, 497.30] | |
8 | Ⅲ | 63.760[23.80, 148.3] | 128.97[68.50, 219.8] | 176.05[108.0, 265.6] | 397.420[301.1, 493.50] |
Ⅳ | 88.150[41.70, 179.6] | 154.13[94.00, 243.2] | 197.27[134.4, 280.3] | 376.300[288.6, 454.50] | |
Ⅴ | 118.10[27.33, 216.2] | 192.90[75.76, 296.6] | 241.30[110.4, 352.8] | 444.800[325.4, 602.80] |
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