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transparent;"><span class="11102pt">0引言</span></a></p> <div class="Section1"> <p class="20" style="margin:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:24.0pt;line-height:16.1pt;mso-line-height-rule: exactly;background:transparent"><span class="2">在加工中心的可靠性估计中，参数估计作为重要组 成部分，可通过样本信息对总体分布所包含的未知参数 进行估计，从而推断出分析对象的实际分布情况，其估 计的准确性将直接影响可靠性评价判断的权威性，在分 析对象全寿命周期过程的可靠性活动中发挥重要作用。 因此，参数估计方法的精确度和易算度是评价其优劣的 .重要指标，受到了广泛关注和研究。</span></p> <p class="20" style="margin:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:24.0pt;line-height:16.1pt;mso-line-height-rule: exactly;background:transparent"><span class="2">长期以来，国内外学者就参数估计问题进行了大量 研究，针对可靠性数据特点提出了满足不同分布条件的 估计算法，比如矩估计法、极大似然法、贝叶斯方法， 并不断加以改进。</span><span class="2"><span style="font-family: 'Times New Roman';">£</span></span><span class="2">88611\</span><span class="2"><span style="font-family: 'Times New Roman';">¥</span></span><span class="2">311§61'<sup>[1]</sup>提出利用矩估计法求解 三参数估计问题，并给出针对</span><span class="2Georgia"><span lang="EN-US">weibull</span></span><span class="2">分布的参数值表； </span><span class="2Georgia"><span lang="EN-US">HL</span></span><span class="2"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2Georgia"><span lang="EN-US">Hartei</span></span><span class="2">•等人嗎出了基于完整和截尾样本的三参数</span><span class="2Georgia"><span lang="EN-US">Gam</span></span><span class="2"><span style="font-family: SimSun;">- </span></span><span class="2Georgia"><span lang="EN-US">ma</span></span><span class="2">分布和</span><span class="2Georgia"><span lang="EN-US">weibull</span></span><span class="2">分布的极大似然法，通过求解3个联 立的极大似然方程即可得到未知参数估计值；在此基础 上，</span><span class="2Georgia"><span lang="EN-US">GlenH</span></span><span class="2"><span style="font-family: SimSun;">. </span></span><span class="2Georgia"><span lang="EN-US">Lemon<sup>ra</sup></span></span><span class="2">提出左、右删失数据条件下三参数 </span><span class="2Georgia"><span lang="EN-US">weibull</span></span><span class="2">分布的极大似然估计法，将3个联立方程简化为 2个联立方程求解，降低了计算难度；曲延碌等人</span><span class="2"><sup><span style="font-family: SimSun;">w</span></sup></span><span class="2">基于 基金项目：“高档数控机床与基础制造装备”国家科技重 大专项，课题《千台国产加工中心可靠性提升工程》 (课题编号：</span><span class="2"><span style="font-family: SimSun;">2013</span></span><span class="2Georgia"><span lang="EN-US">ZX</span></span><span class="2"><span style="font-family: SimSun;">04011-011)</span></span></p> <p class="20" style="margin-top:0cm;margin-right:0cm;margin-bottom:0cm; margin-left:12.0pt;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph; text-indent:0cm;line-height:16.1pt;mso-line-height-rule:exactly;background: transparent"><span class="2">同一思想，通过设定位置参数值，采取只含有其余两个 参数的极大似然方程组代替三参数最大似然方程组。贝 叶斯方法则利用先验分布，在小样本实验数据条件下展 现优势，自创建以来得到了大量研究与改进，比如 </span><span class="2Georgia"><span lang="EN-US">George</span></span><span class="2"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2Georgia"><span lang="EN-US">C</span></span><span class="2"><span style="font-family: SimSun;">. </span></span><span class="2Georgia"><span lang="EN-US">Canavos</span></span><span class="2">等人</span><span class="2"><sup><span style="font-family: SimSun;">[S1</span></sup></span><span class="2">通过设定独立的先验分布给出 </span><span class="2Georgia"><span lang="EN-US">weibull</span></span><span class="2">分布尺寸参数与形状参数的贝叶斯估计方法，</span><span class="2Georgia"><span lang="EN-US">S</span></span><span class="2"><span style="font-family: SimSun;">. </span></span><span class="2Georgia"><span lang="EN-US">K</span></span><span class="2"><span style="font-family: SimSun;">. </span></span><span class="2Georgia"><span lang="EN-US">Sinha</span></span><span class="2">等人提出三参数</span><span class="2Georgia"><span lang="EN-US">weibull</span></span><span class="2">分布下的参数估计和 可靠性方程的贝叶斯估计法。然而，常规的参数估计方 法不但适用范围局限，且在复杂的数据分布情况下难以 进行求解，同时存在结果粗糙，影响估计精确度的问 题，很难满足实际需求。</span></p> <p class="20" style="margin-top:0cm;margin-right:0cm;margin-bottom:10.85pt; margin-left:0cm;text-align:justify;text-justify:inter-ideograph;text-indent: 31.0pt;line-height:16.1pt;mso-line-height-rule:exactly;background:transparent"><span class="2">粒子群优化算法</span><span class="2"><span style="font-family: SimSun;">(</span></span><span class="2Georgia"><span lang="EN-US">PSOp</span></span><span class="2">是近年来发展起来的一种新 的进化算法。与遗传算法相比，粒子群算法通过追随当 前搜索到的最优值来寻找全局最优。局部最优粒子群优 化算法是粒子群优化算法的延伸，其不但继承</span><span class="2Georgia"><span lang="EN-US">PS</span></span><span class="2"><span style="font-family: SimSun;">0</span></span><span class="2">算法 的实现容易、精度高、收敛快、通用性广等优点，还有较 !强的全局最优解搜索能力，但是关于直接用该算法进行 可靠性模型参数估计的论文很少。因此，本文提出了一种 用该算法对加工中心可靠性模型进行参数估计的方法。</span></p> <p class="11100" style="margin-top:0cm;margin-right:0cm;margin-bottom:5.75pt; margin-left:12.0pt;text-align:justify;text-justify:inter-ideograph;text-indent: 0cm;line-height:10.0pt;mso-line-height-rule:exactly;background:transparent"><a name="bookmark2"><span class="11101pt">1加工中心的可靠性模型</span></a></p> <p class="20" align="left" style="margin: 0cm 0cm 0.0001pt; text-indent: 31pt; line-height: 15.85pt; background: transparent;"><span class="2">经过查阅大量相关材料和文献易知</span><span class="2"><sup><span style="font-family: SimSun;">[w</span></sup></span><span class="2"><sup><span lang="EN-US">〇</span></sup></span><span class="2"><sup><span style="font-family: SimSun;">1</span></sup></span><span class="2"><span lang="EN-US">，</span></span><span class="2">加工中心 故障时间间隔服从三参数威布尔分布，则加工中心故障 |密度函数（即三参数威布尔分布函数</span><span class="2"><span lang="EN-US">）</span></span></p> <p class="150" style="margin-top:0cm;margin-right:0cm;margin-bottom:4.85pt; margin-left:0cm;text-align:justify;text-justify:inter-ideograph;line-height: 10.0pt;mso-line-height-rule:exactly;background:transparent"> </p> <p class="11100" style="margin-top:0cm;margin-right:0cm;margin-bottom:2.3pt; margin-left:14.0pt;text-align:justify;text-justify:inter-ideograph;text-indent: -14.0pt;line-height:10.0pt;mso-line-height-rule:exactly;background:transparent"><a name="bookmark4"><span class="11104pt">5结论</span></a></p> <p class="20" style="margin-top:0cm;margin-right:12.0pt;margin-bottom:0cm; margin-left:0cm;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph; text-indent:22.0pt;line-height:16.1pt;mso-line-height-rule:exactly;background: transparent"><span class="20pt">本文提出了一种运用</span><span class="2Georgia"><span lang="EN-US">Lbest</span></span><span class="20pt"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2Georgia"><span lang="EN-US">PSO</span></span><span class="20pt">算法进行加工中心 可靠性估计的方法。同时在</span><span class="2Georgia"><span lang="EN-US">LbestPSO</span></span><span class="20pt">的基础上引人了 变异操作和自适应调整惯性因子，提高了 </span><span class="2Georgia"><span lang="EN-US">Lbest</span></span><span class="20pt"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2Georgia"><span lang="EN-US">PSO</span></span><span class="20pt">算 法的全局解搜索能力和局部改良能力。实验结果表明，</span><span class="20pt">改进后的</span><span class="2Georgia"><span lang="EN-US">LbestPSO</span></span><span class="20pt">算法在大小样本中都具有较髙的估 计精度，并且其收敛速度快，时间成本小。</span></p> <p class="20" style="margin-top:0cm;margin-right:12.0pt;margin-bottom:0cm; margin-left:0cm;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph; text-indent:22.0pt;line-height:16.1pt;mso-line-height-rule:exactly;background: transparent"> </p> <p class="20" style="margin-top:0cm;margin-right:12.0pt;margin-bottom:0cm; margin-left:0cm;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph; text-indent:22.0pt;line-height:16.1pt;mso-line-height-rule:exactly;background: transparent"><span style="font-size:9.0pt;font-family:宋体;mso-ascii-font-family: "Times New Roman";mso-hansi-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman";mso-font-kerning:1.0pt;mso-ansi-language:EN-US;mso-fareast-language: 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