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bodyBgs[2] = "/images/banner/s3.jpg"; bodyBgs[3] = "/images/banner/s4.jpg"; bodyBgs[4] = "/images/banner/s5.jpg"; var randomBgIndex = Math.round( Math.random() * 4 ); //輸出隨機的背景圖 document.write('<style>.banner{background:url(' + bodyBgs[randomBgIndex] + ') no-repeat 50% bottom}</style>'); //]]> </script> <section> <div class="banner" style="height:160px;"> </div> </section> <!-- 面包屑導航開始 --> <div class="container "> <nav class="sub_menu"> <ol class="breadcrumb"><span class="glyphicon glyphicon-list"></span> <li><a href="/">網站首頁</a></li> <li><a title="數控文章" href="/bethel/news">數控文章</a></li> </ol> </nav> </div> <!--/sub_menu--><!-- 面包屑導航結束 --> <!-- 頁面標題開始 --> <div class="title"> <h3><a>鏈式刀庫及機械手分布模型的建立<small></small></a></h3> </div> <!-- 頁面標題結束 --> <hr class="featurette-divider"> <!-- 中部開始 --> <div class="container"> <!-- 正文 --> <div class="lead" id="print"> <p> <a name="bookmark51" style="line-height: 14pt; background-color: transparent;"><span class="7"><span style="font-family: SimSun;">5.3</span></span></a><span class="7" style="line-height: 14pt; background-color: transparent;">分布模型的建立</span></p> <p class="21" style="margin-bottom:18.95pt;text-align:justify;text-justify:inter-ideograph; text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule:exactly;background: transparent"><span class="2">通過對直方圖的分析，并考慮威布爾分布較強的適應性。所以下文將在假設 故障數據服從兩參數威布爾模型的基礎上，對故障數據進行處理，并進行相應的 參數估計，最后運用解析法進行模型檢驗，從而最終確定故障數據所服從的分布 模型。</span></p> <p class="830" style="margin-top:0cm;margin-right:0cm;margin-bottom:18.8pt; margin-left:0cm;line-height:14.0pt;mso-line-height-rule:exactly;mso-pagination: lines-together;page-break-after:avoid;background:transparent"><st1:chsdate year="1899" month="12" day="30" islunardate="False" isrocdate="False" w:st="on"><span class="83-2pt"><span lang="EN-US">5.3.4</span></span></st1:chsdate><span class="83">分布模型的擬合分析</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">至此，故障數據的數據分析，模型選擇，參數估計，模型檢驗，均己完成， 分析出該批故障數據服從兩參數的威布爾分布。</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">為更好的驗證該批數據是否服從威布爾分布，下文通過對比觀測值與擬合曲 線、觀測值與威布爾分布模型來最終分析該模型是否合理。</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">如下圖</span><span class="2"><span style="font-family: SimSun;">5.8</span></span><span class="2">為觀測值的自然對數散點圖與最小二乘法擬合圖在同一張圖中顯 示的擬合圖。如下圖</span><span class="2"><span style="font-family: SimSun;">5.9</span></span><span class="2">為觀測值的經驗分布散點圖與兩參數威布爾分布曲線圖 在同一張圖中顯示的擬合圖。</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">從下圖可以看出，觀測值的自然對數散點均落在了線性擬合直線周圍，觀測 值的經驗分布散點也均落在威布爾分布曲線周圍，由此可以再次判斷該批數據服 從威布爾分布模型。</span></p> <p class="70" align="left" style="margin: 0cm 0cm 16.4pt; line-height: 14pt; page-break-after: avoid; background: transparent;"><a name="bookmark59"><span class="7"><span style="font-family: SimSun;"><o:p> </o:p></span></span></a></p> <p class="70" align="left" style="margin: 0cm 0cm 16.4pt; line-height: 14pt; page-break-after: avoid; background: transparent;"><span class="7"><span style="font-family: SimSun;">5.5</span></span><span class="7">實驗室可靠性試驗數據分析</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:23.3pt;mso-line-height-rule: exactly;background:transparent"><span class="2">在完成對現場數據的處理之后，下一步是進行試驗數據的處理，根據上文所 述及以往的故障間隔時間數據處理經驗，兩參數的威布爾分布是一種很好的選擇。 通過本章的表</span><span class="2"><span style="font-family: SimSun;">4.2</span></span><span class="2">的實驗室試驗數據可知，由于試驗數據較少，傳統的估計方法 很難進行，貝葉斯理論方法考慮到了參數的先驗分布，因此使用貝葉斯理論方法 進行小樣本數據的處理優勢較大<sup>[6()]</sup>。因此，下文將用貝葉斯理論方法對故障數據 進行處理。參數的估計使用專用軟件</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WINBUGS</span></span><span class="2">進行估計。</span></p> <p class="21" align="left" style="margin: 0cm 0cm 0.0001pt 32pt; text-indent: 0cm; line-height: 23.3pt; background: transparent;"><!--[if !supportLists]--><span style="font-family: SimSun;">(1)<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><!--[endif]--><span class="22pt">貝葉斯理論</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">貝葉斯理論是</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Tomas</span></span><span class="2"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Bayes</span></span><span class="2">提出的理論方法。貝葉斯方法與經典極大似然方 法一起構成了現代統計學的主要推斷手段。在貝葉斯分析中參數是一個隨機變量， 并且此參數可以用一個概率分布對其進行描述。此概率分布的模型是在進行抽樣 之前就預先知道的，是關于參數先驗信息的概率描述，稱之為先驗分布。先驗分 布和似然函數一起構成了貝葉斯分析理論。似然函數也稱為聯合密度函數，它綜 合了總體信息和樣本信息。</span></p> <p class="21" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;background:transparent"><span class="2">如果僅憑先驗分布只能對參數進行推斷，不能定量，在貝葉斯后期的發展過程中，便形成了貝葉斯公式的密度函數形式。</span><span class="2"><o:p></o:p></span></p> <p class="110" align="left" style="margin: 0cm 0cm 4.35pt 150pt; text-indent: 0cm; line-height: 11.5pt; background: transparent;"><span lang="EN-US"> </span></p> <p class="110" style="margin-top:0cm;margin-right:0cm;margin-bottom:0cm; margin-left:32.0pt;margin-bottom:.0001pt;text-indent:0cm;line-height:11.5pt; mso-line-height-rule:exactly;mso-list:l0 level1 lfo1;tab-stops:58.3pt; background:transparent"><!--[if !supportLists]--><span lang="EN-US" style="font-size: 11pt; font-family: SimSun;">(2)<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><!--[endif]--><span class="11"><span lang="EN-US">WINBUGS</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">軟件</span></span></p> <p class="110" style="margin-top:0cm;text-indent:26.0pt;line-height:20.15pt; mso-line-height-rule:exactly;background:transparent"><span class="11"><span lang="EN-US">BUGS</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">是</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;"> </span></span><span class="11"><span lang="EN-US">Bayesian</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11"><span lang="EN-US">Inference</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11"><span lang="EN-US">Using</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11"><span lang="EN-US">Gibbs</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11"><span lang="EN-US">Sampling</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">的縮寫</span></span><span class="11"><span lang="EN-US">，WINBUGS</span></span><span class="11SimSun"><span lang="EN-US" style="font-size: 11pt;"> </span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">是在</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;"> </span></span><span class="11"><span lang="EN-US">BUGS</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">的基礎上</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">，</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">開發的</span></span><span class="11"><span lang="EN-US">Windows</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">軟件版本。</span></span><span class="11"><span lang="EN-US">WINBUGS</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">軟件是利用</span></span><span class="11"><span lang="EN-US">MCMC</span></span><span class="11SimSun"><span style="font-size: 11pt; font-family: 宋體;">方</span></span></p> <p class="21" align="left" style="margin-bottom: 0.0001pt; line-height: 20.15pt; background: transparent;"><span class="2">法對復雜統計模型進行貝葉斯統計分析的專用軟件。</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WINBUGS</span></span><span class="2">數據處理的基本 原理:通過</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Gibbs</span></span><span class="2"><span lang="EN-US" style="font-family: SimSun;"> </span></span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Sampling</span></span><span class="2">和</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Metropolis</span></span><span class="2">算法，在完全條件概率分布中進行抽樣， 從而生成相應的馬爾可夫鏈，最終完成對模型參數的估計。它主要用于分布函數 無法寫出或者分布函數不能清楚寫出的情況。這也是本文引用這種軟件進行試驗 數據處理的主要原因。</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WINBUGS</span></span><span class="2">不僅可以方便用于復雜分布模型進行</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">Gibbs</span></span><span class="2">抽 樣，還能直觀描述簡單的有向圖模型，并給出相關參數的抽樣動態圖。</span></p> <p class="21" style="margin-top:0cm;margin-right:0cm;margin-bottom:0cm; margin-left:32.0pt;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph; text-indent:0cm;line-height:11.5pt;mso-line-height-rule:exactly;mso-list:l0 level1 lfo1; background:transparent"><!--[if !supportLists]--><span style="font-family: SimSun;">(3)<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><!--[endif]--><span class="22pt">使用</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WINBUGS</span></span><span class="2">軟件進行試驗數據分析</span></p> <p class="21" style="margin:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:20.4pt;mso-line-height-rule: exactly;mso-list:l1 level1 lfo2;tab-stops:45.85pt;background:transparent"><!--[if !supportLists]--><span style="font-family: SimSun;">1)<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><!--[endif]--><span class="2">% 取值范圍的確定</span></p> <p class="21" align="left" style="margin-bottom: 6.4pt; text-indent: 26pt; line-height: 20.4pt; background: transparent;"><span class="2">在使用</span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WINBUGS</span></span><span class="2">軟件進行參數估計之前，首先要編寫軟件能識別的模型， 在此模型程序編寫中，比較重要的是威布爾分布的兩個參數％ </span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">P</span></span><span class="2">的取值區間的 建立。根據以往數據的處理經驗。</span></p> <p class="21" style="margin:0cm;margin-bottom:.0001pt;text-align:justify; text-justify:inter-ideograph;text-indent:26.0pt;line-height:19.9pt;mso-line-height-rule: exactly;mso-list:l1 level1 lfo2;tab-stops:47.05pt;background:transparent"><!--[if !supportLists]--><span style="font-family: SimSun;">2)<span style="font-stretch: normal; font-size: 7pt; line-height: normal; font-family: 'Times New Roman';"> </span></span><!--[endif]--><span class="2">數據處理及模型檢驗</span></p> <p class="21" style="margin-bottom:25.75pt;text-align:justify;text-justify:inter-ideograph; text-indent:26.0pt;line-height:11.5pt;mso-line-height-rule:exactly;background: transparent"><span class="2">確定《^的取值，并編寫完成相應的威布爾分布模型的程序之后，就可以 進行相應的計算了。為了使最終的結果更加準確，我們使軟件隨機輸出10000完 變量。同時，為了驗證最終模型的收斂性，我們使用3條馬爾可夫鏈進行對比。</span><br clear="all" style="mso-special-character:line-break;page-break-before:always" /> <i><span style="font-family: 'Times New Roman';"><o:p></o:p></span></i></p> <p class="830" style="margin-top:0cm;margin-right:0cm;margin-bottom:16.05pt; margin-left:0cm;line-height:14.0pt;mso-line-height-rule:exactly;mso-pagination: lines-together;page-break-after:avoid;background:transparent"><a name="bookmark61"><span class="83"><span style="font-family: SimSun;">5.7</span></span></a><span class="83">本章小結</span></p> <p><span class="2"><span style="font-size: 11pt;">本章節分析處理了所收集的鏈式刀庫及機械手現場可靠性試驗數據，得出了 現場可靠性試驗數據所服從的威布爾分布模型，得到了鏈式刀庫及機械手在現場試驗時的可靠性水平。在此過程中， 進行了可靠性數據的初步分析、分布模型的參數估計、分布模型的線性相關性檢 驗和假設檢驗，并進行了模型的擬合分析。在實驗室試驗數據較少的情況下，采 用了貝葉斯統計分析方法，引用了 </span></span><span class="2TimesNewRoman"><span lang="EN-US" style="font-size: 11.5pt;">WinBUGS</span></span><span class="2"><span style="font-size: 11pt;">專業軟件進行數據處理，</span></span><span class="22pt"><span style="font-size: 11pt;">得到了鏈式刀庫及機械手的可靠性水平，</span></span><span class="2"><span style="font-size: 11pt;">通過現場試驗數據處理結果和實驗室試驗數據處理結果的對 比，得出了在排除早期故障的前提下，實驗室試驗可以近似代替現場試驗。</span></span></p> <p> </p> <p> </p> <p class="MsoNormal"><span style="font-size:9.0pt;font-family:宋體;mso-ascii-font-family: "Times New Roman";mso-hansi-font-family:"Times New Roman"">本文采摘自“<a href='http://www.seo-service24.com/' target='_blank'>加工中心</a>鏈式刀庫及機械手可靠性試驗方法研究”，因為編輯困難導致有些函數、表格、圖片、內容無法顯示，有需要者可以在網絡中查找相關文章！本文由<a href='http://www.seo-service24.com/' target='_blank'>伯特利</a>數控整理發表文章均來自網絡僅供學習參考，轉載請注明！</span><span lang="EN-US" style="font-size:9.0pt"> <o:p></o:p></span></p></div> <div class="blank30"></div> <!-- tag --> <div class="blank10"></div> 標簽： <a href='/index.php?case=tag&act=show&tag=%E5%8A%A0%E5%B7%A5%E4%B8%AD%E5%BF%83' target='_blank'>加工中心</a> <a href='/index.php?case=tag&act=show&tag=%E6%95%B0%E6%8E%A7%E5%8A%A0%E5%B7%A5%E4%B8%AD%E5%BF%83' target='_blank'>數控加工中心</a> <a href='/index.php?case=tag&act=show&tag=CNC%E5%8A%A0%E5%B7%A5%E4%B8%AD%E5%BF%83' target='_blank'>CNC加工中心</a> <a href='/index.php?case=tag&act=show&tag=%E6%95%B0%E6%8E%A7%E6%9C%BA%E5%BA%8A' target='_blank'>數控機床</a> <a href='/index.php?case=tag&act=show&tag=%E5%88%80%E5%BA%93' target='_blank'>刀庫</a> <!-- 分類 --> <div class="blank10"></div> 分類： <a href='/bethel/type/jnc/type-1.html' target='_blank'>加工中心</a> <div class="blank30"></div> 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target='_blank'>數控機床</a> <a href='/index.php?case=tag&act=show&tag=%E5%88%80%E5%BA%93' target='_blank'>刀庫</a> <small>相關內容</small></a></h3> <p></p> <span>——</span> </div> <!-- 頁面標題結束 --> <hr class="featurette-divider"> <div class="container list-container"> <!-- 內容列表開始 --> <div class="news-list list-border list-border-w"> <div class="list-date"> <span>17</span> <p>2019-08</p> </div> <h4><a title="五面體加工中心" href="/bethel/cnc/show_2614.html" target="_blank">五面體加工中心</a></h4> <p>產品規格Product specifications項目單位V11數控系統三菱 M80B行程X軸行程(工作臺左右)mm1100Y軸行程(主軸頭上下)mm600Z軸行程(工作臺前后)mm700主軸端面~工作中心距離mm180~780主軸中心~工作臺面距離50~750旋轉工作臺工作臺…… <a href="/bethel/cnc/show_2614.html" target="_blank">[了解更多]</a></p> </div> <div class="news-list list-border list-border-w"> <div class="list-date"> <span>19</span> <p>2019-09</p> </div> <h4><a title="動梁龍門的同步控制、同步消隙、全閉環控制" href="/bethel/news/show_2506.html" target="_blank">動梁龍門的同步控制、同步消隙、全閉環控制</a></h4> <p>0 引言 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