滾杠式紅棗分級機的設計【紅棗分層機-棗子分選機】

滾杠式紅棗分級機的設計【紅棗分層機-棗子分選機】,紅棗分層機-棗子分選機,滾杠式,紅棗,分級,設計,分層,棗子,分選 ? 湖 南 農 業(yè) 大 學 全日制普通本科生畢業(yè)論文 ? 滾杠式紅棗分級機 THE ROLL BAR JUJUBE GRADE? ? 學生姓名： 學 號： 年級專業(yè)及班級: 指導老師及職稱： 學 院： 工學院 ? ? ? 湖南·長沙 提交日期：2013年5月 湖南農業(yè)大學全日制普通本科生畢業(yè)設計 誠 信 聲 明 本人鄭重聲明：所呈交的本科畢業(yè)設計是本人在指導老師的指導下，進行研究工作所取得的成果，成果不存在知識產權爭議。除文中已經注明引用的內容外，本論文不含任何其他個人或集體已經發(fā)表或撰寫過的作品成果。對本文的研究做出重要貢獻的個人和集體在文中均作了明確的說明并表示了謝意。本人完全意識到本聲明的法律結果由本人承擔。 畢業(yè)設計作者簽名： 年 月 日 目 錄 摘要……………………………………………………………………………1 關鍵詞……………………………………………………………………………1 1前言……………………………………………………………………………2 1.1選題意義和目的及我國發(fā)展現狀…………………………………………2 1.2存在的問題和主要研究內容……………………………………………3 1.3可行性分析………………………………………………………………3 2紅棗分級機的組成和原理…………………………………………………4 3紅棗分級機的機械系統(tǒng)設計…………………………………………………4 3.1滾桿…………………………………………………………………………4 3.1.1強度校核…………………………………………………………………5 3.1.2剛度校核…………………………………………………………………6 3.2電動機的選擇………………………………………………………………6 3.3減速器的選擇………………………………………………………………7 3.4鏈條設計……………………………………………………………………7 3.4.1結構特點…………………………………………………………………7 3.4.2鏈傳動設計計算…………………………………………………………8 3.5高速軸設計………………………………………………………………11 3.6低速軸設計………………………………………………………………17 3.7分級機機架設計…………………………………………………………22 4總結…………………………………………………………………………24 5參考文獻 ……………………………………………………………………25 致謝……………………………………………………………………………26 專家系統(tǒng)與應用程序 基于模糊集理論的有效性的評估農業(yè)機械 摘要： 農業(yè)機械生產的服務質量是代表農業(yè)成功的基本因素之一。從這個意義上說,有一個明確需要定義這些機器質量的具體指標,它有可能決定哪些機器適合不同工作條件。服務的技術系統(tǒng)概念的有效性代表質量的一個綜合指標。本文運用模糊集理論定義的有效性和可靠性、可維護性和功能作為影響指標的有效性。在這個意義上的模型評估的有效性拖拉機作為農業(yè)的典型代表機器已經形成。本模型是基于集成上述的語言描述。利用模糊集理論和max-min成分影響指標，模型進行了測試。同一類別的三個拖拉機為例,利用的氣候和土壤條件在更廣泛的貝爾格萊德(塞爾維亞)地區(qū)。即使在這個實驗中條件是非常重要參數 , 相比于其他操作，實現的效果差異也達到大致相等 。 1.介紹 為達到擴張的全球農產品的要求，實現更大的農業(yè)技術的發(fā)展。人們普遍認識到當代農業(yè)系統(tǒng)中需要適當的機器和設備，仔細和詳細規(guī)劃的需求和控制所有相關的生物、技術、技術和其他進程。最終結果的準確、可靠的預測為每個指定的操作,以及完整的作物生產過程中,。要求加強了引入復雜的實驗,數學,農業(yè)科學統(tǒng)計,機械和其他方法都是特別重要的。在過去的幾十年。除了上述的要求,一個適當的技術體系必須滿足生產力的標準,期望的作物生產。在大多數情況下,在塞爾維亞，tractor-machinery農場系統(tǒng)的能力遠遠超過最優(yōu)級別(尼克里奇′,2005),增加成本作物生產。目前,現有的數學優(yōu)化方法、支持的高性能計算機有效地解決優(yōu)化問題(Dette &韋伯達菲et al .,1990;1994;Mileusnic′,2007;等等)。一個最優(yōu)的技術體系的形成為我們生產了更便宜的食品,高度影響拖拉機的可靠性、可維護性和系統(tǒng)的功能。 與系統(tǒng)科學發(fā)展同樣,實際上的開始是IIWorld戰(zhàn)爭后,在適當的工程和科學文獻定義了一系列的概念,來描述技術系統(tǒng)的基本特征的點的服務質量?？煽啃缘闹笜耸羌夹g系統(tǒng)和行為操作,技術指標和可維護性systembehaviors期間的失敗可以表示為大多數可辨認的概念。這兩個概念及其實現最先進的發(fā)展。有效性的概念被定義在試圖描述同時技術操作系統(tǒng)的行為和失敗的時期。這概念考慮可靠性和可用性的表演,以及提出了技術系統(tǒng)設計的功能(Papic&Milovanovic2007)。換句話說,一個技術系統(tǒng)的有效性的概率,一個成功的功能系統(tǒng)技術和執(zhí)行所需的準則函數限制允許的差異對于給定時間和給定的周圍條件。雖然在相同的精神,一些作者定義有效性有所不同。在(Ebramhimipour &鈴木,2006)被定義為總體有效性的指標包含效率、可靠性和可用性。這兩個引用定義包括并行關于可靠性和可用性,雖然可用性包括可靠性和可維護性(Ivezic′,Tanasijevic′,& Ignjatovic′,2008)。因此它可以商定有效性是影響可靠性、可維護性的功能。可靠性系統(tǒng)不斷的被定義為特征保持操作abilitywithin允許的差異極限在現在;可維護性的能力是預防和發(fā)現故障及損壞,系統(tǒng)更新通過參加技術和操作能力和功能維修,功能實現功能的程度要求,即調整環(huán)境,或更準確系統(tǒng)運行的條件。 監(jiān)測的可靠性和可維護性是常見的監(jiān)控時間的狀態(tài)顯示(圖1)可靠性和可維護性的函數可以確定,以及操作的平均時間和平均時間相關。主要問題出現在形成時間的照片數據監(jiān)控和記錄。在現實條件的機器應該連接到信息系統(tǒng)將準確記錄每一個失敗、持續(xù)時間和修復程序。這通常是昂貴或簡易監(jiān)測機器的性能,即關閉的,是不精確的。此外,提供的統(tǒng)計數據處理時間的狀態(tài)要求所有的機器在平等的條件下工作,這是難以實現。至于技術體系的功能,沒有共同的方法測量和量化。這在本文的原因,為了評估的有效性, 將使用專業(yè)知識和分析機器判斷工作的工作過程。應用專業(yè)知識判斷主要用于文學,主要是為數據處理和評估的技術系統(tǒng)而言:風險(Li 廖,2007)、安全(王2000;王、楊、&森1995)或可靠性，用專業(yè)知識判斷自然的語言形式。因此,數學和邏輯概念模型進行處理的經驗判斷,即計算的語言描述,模糊集合理論使用(Klir &元,1995;枝,1996)。應用模糊今天集代表了最常用的工具之一各領域解決問題的優(yōu)化(黃顧,&杜,2006)和識別(陳,1996)過程問題。Cai(1996)提出了不同的概述應用程序方面的模糊方法在系統(tǒng)失敗工程,這是一個接近效能評估問題。應用模糊邏輯理論和專家系統(tǒng)(遼、一般2011;Liebowitz,1988)也用于解決優(yōu)化問題的農業(yè)機械領域。(Abbaspour-Fard Rohani & Abdolahpour,2011)的基礎上神經網絡的應用程序,在拖拉機預測失敗。(Yu,你們&趙,2010)模糊數學、可靠性理論和多目標優(yōu)化技術應用設計拖拉機最終傳動。機器的可預測性和可靠性,顯著依賴于其有效性的技術系統(tǒng)。本文的觀點是根據模糊集理論的利用率建立模型的有效性。從而說明模糊集是用于分析可靠性、可維護性和功能表現(部分指標的有效性)以及為他們融入效率。他們的工作是以這種有效模型質量的方式評估技術系統(tǒng)。模型可以作為標準購買決策相關的任何程序,系統(tǒng)的操作或維護,修理的預測和維護成本。質量和功能的建議模型有效性的確定農業(yè)所示機械、拖拉機。 2。基于模糊集的有效性表現評估理論 數學和概念模型的有效性評估實際上是在兩個步驟:總結模糊命題的部分的效性指標;模糊提到的分成一個指標——合成。模糊命題過程為代表的聲明,包括語言變量基于可用的信息技術系統(tǒng)。在這個意義上它必須定義語言的名字變量,代表不同的等級的效果考慮技術系統(tǒng)和定義的模糊集描述提到的變量。作文是一個模型,它提供了影響結構有效性性能的指標。 2.1。模糊模型解決問題 第一步創(chuàng)建的模糊有效性模型(E)評估本身和定義語言變量以及可靠性(R)、可維護性(M)和功能(F)有關.許多語言變量,它可以發(fā)現最大數量的理性,人類可辨認的表達式可以同時識別(王et al .,1995)。然而,識別的考慮甚至較小的特征數量的變量可以有用,因為專家的判斷(Ivezic′et al .,2008)模糊集的靈活性一般包括過渡現象。根據以上,五個語言變量為代表的有效性表現包括:窮,充足,平均,和優(yōu)秀。這些語言形式變量給出適當的三角模糊集(Klir 元,1995),圖2所示。 在圖2中,j = 1,。實際上,5代表的計量單位有效性。因此,部分指標的有效性:R、M和F,隸屬函數l:在下一步中,執(zhí)行max-min組成。馬克斯-敏成分,也稱為悲觀,經常用于模糊代數作為一個綜合模型(Ivezic′et al .,2008;Tanasijevic et al .,2011;王王et al .,1995;2000)。這個想法是為了讓整體評估(E)等于部分虛擬代表評估。這評估被確定為之間的最好的一個最壞的打算部分成績(R、M或F)。 它可以得出的結論是,所有的元素(R、M和F)E有同等影響E,max-min組成以并行方式被使用,這將部分的到綜合指標。在文學(Ivezic′et al .,2008;etal .,1995)max-min成分通過運營商”和“和”或“提供一個優(yōu)勢在其他的某些元素在合成的過程中,也使用。 準確地說,如果我們看看三個部分指標,即他們的隸屬函數(1),可以使C:= j3 = 53組合 的隸屬度函數。每一種組合代表一個可能的合成效果評估(E)。 這個表達式(6)有必要映射回E模糊集(圖2)。最佳(王et al .,1995),用于轉換方法E描述(6)形成定義等級的會員模糊集:貧窮、充足,平均,和優(yōu)秀的好。這個過程被公認為識別。最佳方法是使用距離E(d)之間通過“max-min”成分(6)和每個人E表達式(根據圖2)來表示的程度E是確認每個模糊集的有效性(圖2)。越接近勒(6)是第i個語言變量,小迪。距離di等于零,如果勒(6)只是第i個相同隸屬度函數的表達式。在這種情況下,E不應該評估其他表達式,由于這些表達式的排他性。假設迪民(i = 1,。,5)是最小的距離對Ej,讓a1,。,a5代表相對的倒數距離(計算相應的比率距離di(7)和迪民提到的值)。然后,人工智能 : 1.一個說明性的例子 作為一個說明性的例子對農業(yè)機械的評價有效性,比較分析三個拖拉機A1 B2、本文給出和C2。 在拖拉機7.146 l發(fā)動機LO4V TCD 2013安裝。謝謝從35%的扭矩儲備,拖拉機是能夠滿足所有需求預期表現最差的農業(yè)操作在農業(yè)?？偼侠瓩C質量是16000公斤。根據經濟合作與發(fā)展組織(代碼2)報告最大動力輸出軸功率測量在2200轉243千瓦的燃油消耗率嗎198 g /千瓦小時(ECE-R24)。發(fā)動機的最大扭矩1482海里在引擎1450 rpm的政權。傳動裝置是精心“不一樣的”傳達。事業(yè)聯動機制是一個類別II / III與提升11800公斤。 在拖拉機B2和C2 8.134 l發(fā)動機6081 hrw37 JD安裝,儲備扭矩的40%,這能夠滿足所有的拖拉機需求預期表現最差的農業(yè)在農業(yè)操作。拖拉機總重量是14000公斤。根據經合組織(代碼2)報告最大的權力來衡量動力輸出軸在2002轉217千瓦燃料消耗率193克/千瓦小時(ECE-R24)。在發(fā)動機最大扭矩1320海里轉速為1400 rpm。傳播是“AutoPower。聯動機制是一個類別II / III 10790丹的提升力。 兩個模型都是電子控制拖拉機發(fā)動機和燃料供給系統(tǒng),滿足排放法規(guī)。從提交的技術特點的拖拉機,B和C看到所有三個拖拉機全功能forperforming困難操作不同的農業(yè)技術生產。拖拉機B和C有相同的技術特征,和實踐是相同的類型和模式,除了拖拉機B進入操作在2007年5月,一輛拖拉機C 6月2007年。一輛拖拉機實驗農場,這是技術文檔的基本模型,在7月份進入操作2009年。保持農業(yè)技術的主要任務提供功能和機器的可靠性。維護所有三個拖拉機是通過機器商店所擁有的用戶升級選擇。 十個工程師(分析師)致力于維護和操作拖拉機的采訪。他們評價R,D和F表1中給出。首先,拖拉機是計算的有效性?？梢钥闯隹煽啃允怯墒姆治鰩熢u為優(yōu)秀(6/10 = 0.6),平均三(0.3)和一樣好(0.1)。以這種方式獲得評估R在表單中，在下一步中,這些評估是映射在模糊集(圖1)為了獲得評估(1)。例如,可靠性在這個例子中確定(11),它是語言0.6變量優(yōu)秀加入重量。 因此,模糊集優(yōu)秀定義為:Rexc=(1/0,1/0,1/0,4/0.25 5/1.0)(據嗎圖1)。這樣的特定的值模糊集優(yōu)秀Rexc0.6 =(1 / 0.6(0),2 / 0.6(0),3 / 0.6(0),4 /(0.25 - 0.6),5 /(1.0 - 0.6)}。剩下的四個語言變量被以同樣的方式對待。最后對于每個j = 1,。5具體隸屬度函數(最后一行,表2)被添加到最后拖拉機可靠性模糊形式(1):這些fuzzificated評估(11)和(12)是合成所必需的評估的有效性,使用max-min邏輯。在這種情況下可以使C = 53 = 125組合,走出48的結果。 第一個結果是組合2-2-3:E2-2-3(0.025,0.05,0.125),哪里X2-2-3 =(2 + 2 + 3)/ 3 = 2(四舍五入為整數)。最小值的隸屬度函數這一結果的是0.025。其他的結果和相應的我的值如表3所示。所有這些結果都可以圍繞尺寸X = 2、3、4和5。拖拉機在很大程度上為0.30065(與30%)評估那么好,拖拉機在很大程度上0.27538(27.5%)評估一樣好,而拖拉機C在很大程度上為0.25468平均(25.5%)評估。它可以得出的結論是,C是最糟糕的,當拖拉機只是稍微比B,特別是如果我們看到的評估為優(yōu)秀的28.8%,而B的程度23.8%的程度。分析了拖拉機可以提出的有效性如圖3。,它可以更清楚地看到,拖拉機的最大的效果。如果這個評估(EA,EB,EC)defuzzificated是重心點計算- Z(Bowles & Pelaez,1995),我們得到了評估的效果如下: 這就意味著在1 - 5(即從貧困的規(guī)模優(yōu)秀)拖拉機是最好的和拖拉機C是最壞的打算。驗證的實現結果,統(tǒng)計分析的可用性,像家庭與有效性概念,已經被使用。那在我們的模型顯示,拖拉機是最好的,和C的壞的效果。在現實中,如果我們分析的可用性,它是看到2904 moto-hours拖拉機在工作3130年可用moto-hours;如果10000 moto-hours計算,在9244年的工作將花費moto-hours。拖拉機B的10004年moto-hours可用,它花9069moto-hours在工作,和拖拉機C 9981可用moto-hours花了9045年的工作。實驗表明,更可靠和有效的拖拉機是少是延遲。在某種程度上,這個初始的優(yōu)勢消滅更糟糕的物流交付備件的時候涉及到拖拉機,拖拉機a . 1100年moto-hours工作可憐的物流在維護希望8個工作日, 一個給定的拖拉機和它極大地影響了可維護性的下降帶來的好處,因此相同的效率(內部技術PKB)總剝削的下降。 1.結論 本文提出一種模型有效性的評估技術系統(tǒng)、精確農業(yè)機械、基于模糊集理論。表現作為整體的有效性指標系統(tǒng)的服務質量,即為整個測量技術系統(tǒng)的可用性?？煽啃?、可維護性和功能表演已經公認的有效性參數或指標。語言可以被任命為形式所有提到的共同特征指標。因此模糊集理論出現自然工具建模的有效性。在本文中,應用模糊集理論,這是必要的定義:語言變量及其描述隸屬函數、模糊規(guī)則的組成和模型集成和去模糊化。模糊的成分即max-min邏輯已經被用于集成的有效性指標有效性的整體性能,最適合集成的方法模糊集的隸屬函數和質心點去模糊化的模糊數的計算數值。Max-min組合模型,它暴露在這篇文章中,沒有以這種方式處理相應的文獻。另外,在案例研究中,模型的模糊化的問卷調查的結果,它代表的正是所積累的方式工程師的知識和技能。 提出的模型可以作為一個簡單的工具的快速估計的有效性即為農業(yè)服務的質量機械、基于專家判斷和估計。在同時,該模型不需要復雜的IT基礎設施。分析實現模糊集和適當的模糊有效性可靠性、可維護性和功能表現可以糾正措施的指導購買的方向嗎的設備,結構調整,改變的維護政策或管理/運營商變更 本文具體分析了三個拖拉機,標志著一個B和C,這表明更高效的拖拉機越頻繁宕機。在某種程度上,這種最初的優(yōu)勢就終止了窮交付備件物流。 感謝 研究工作得到了塞爾維亞共和國教育部和科學界的支持。 Effectiveness assessment of agricultural machinery based on fuzzy sets theoryRajko Miodragovic a, Milo Tanasijevic b, Zoran Mileusnic a, Predrag Jovanc ic baUniversity of Belgrade, Faculty of Agriculture, SerbiabUniversity of Belgrade, Faculty of Mining and Geology, Serbiaa r t i c l ei n f oKeywords:Agricultural machineryEffectivenessFuzzy setMaxmin compositiona b s t r a c tThe quality of service of agricultural machinery represents one of the basic factors for successful agricul-tural production. In this sense, there is a clear need for defining the exact indicator of the quality of thesemachines, according to which it could be possible to determine which machine is optimal for differentworking conditions. The concept of effectiveness represents one of synthesis indicators of the qualityof service of the technical systems. In this paper the effectiveness is defined using the fuzzy set theory,and reliability, maintainability and functionality are used as influence indicators of the effectiveness.In that sense the model for assessing the effectiveness of tractor as a typical representative of agromachinery has been formed. The model is based on integration of linguistic description of the above men-tioned influence indicators using fuzzy set theory and maxmin composition. The model was tested onthe example of three tractors of the same category, which are exploited in climatic and soil conditionsin the wider Belgrade (Serbia) area. Even if the conditions in this experiment were approximately equal,the difference of the achieved effects was attained and very significant, compared to other operationparameters.? 2012 Elsevier Ltd. All rights reserved.1. IntroductionRapid expansion of global demands for agricultural products hascaused much greater development of agricultural technique, apro-pos machines and equipments. It is widely recognized that contem-porary agricultural systems demand careful and detailed planningand control of all relevant biological, technical, technological andother processes. An accurate and reliable predicting of the final out-come for each specified operation, as well as for the complete cropproductionprocess,isofspecialimportance.Demandshaveintensi-fied the introduction of sophisticated experimental, mathematical,statistical, mechanical and other methods in agricultural sciencesduring the last few decades. Besides the demands described above,anadequatetechnicalsystemhastosatisfythecriteriaofproductiv-ity, imposed by the conditions of desired crop production. In mostcases, the capacity of tractor-machinery systems on farms in Serbiais much over the optimal level (Nikolic , 2005), increasing the costsof crop production.Nowadays, the existing mathematical optimiza-tion methods, supported by the high-performance computers, canefficiently resolve the optimization problems (Dette & Weber,1990; Duffy et al., 1994; Mileusnic , 2007; etc.). The formation ofan optimal technical system in order to produce cheaper food,highly impacted reliability of tractors, its maintainability, and thefunctionality of the system.With the beginning of systems sciences development, practicallyafter the II World War, in appropriate engineering and scientific liter-ature a series of concepts have been defined, with the idea to describeessential characteristics of technical systems from the point of theirquality of service. Reliability as the indicator of technical systembehaviors in operation, and maintainability as the indicator of techni-cal system behaviors during the period of failures can be stated as themost recognizable concepts. These two concepts and their implemen-tations had the most progressive development. The concept of effec-tiveness was defined later in attempt to describe simultaneouslytechnicalsystemsbehaviorsinoperationandinperiodsoffailure.Thisconceptconsideredreliabilityandavailabilityperformances,aswellasfunctionalityofproposedtechnicalsystemdesign(Papic&Milovanovic,2007). In other words, the effectiveness of a technical system can bearticulatedasprobabilitythatatechnicalsystemwillbeputinfunc-tionsuccessfullyand performrequiredcriterionfunctionwithinthelimits of allowed discrepancies for given time period and given sur-rounding conditions. Although in the same spirit, some authors havedefined effectiveness somewhat differently. In (Ebramhimipour &Suzuki, 2006) effectiveness was defined as overall indicator whichcontains efficiency, reliability and availability. These two citeddefinitionsinclude parallelconcerningofreliabilityandavailability,althoughavailabilityincludesreliabilityandmaintainability(Ivezic ,Tanasijevic ,&Ignjatovic ,2008).Thereforeitcanbeagreeduponthatthe effectivenessis influenced by reliability, maintainability and func-tionality. Reliability is defined as characteristic of a system to contin-uouslykeepoperatingabilitywithinthelimitsofalloweddiscrepancies0957-4174/$ - see front matter ? 2012 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2012.02.013Corresponding author.E-mail address: tanrgf.bg.ac.rs (M. Tanasijevic ).Expert Systems with Applications 39 (2012) 89408946Contents lists available at SciVerse ScienceDirectExpert Systems with Applicationsjournal homepage: the calendar period of time; maintainability as capacity of thesystemforpreventionandfindingfailuresanddamages,forrenewingoperating ability and functionality through technical attending andrepairs; and functionality as the degree of fulfilling the functionalrequirements, namely the adjustment to environment, or more pre-cisely to the conditions in which the system operates.In the case of monitoring reliability and maintainability it iscommon to monitor the time picture of state (Fig. 1) according towhich the functions of reliability and maintainability can be deter-mined, as well as the mean time in operation and the mean time infailure.The main problem that occurs in forming the time picture ofstate is data monitoring and recording. In real conditions the ma-chines should be connected to information system which wouldprecisely record each failure, duration and procedure of repair. Thisis usually expensive and improvised monitoring of the machineperformance, namely of its shut downs, is imprecise. Moreover,statistical data processing provided by the time picture of the staterequires that all machines work under equal conditions, which isdifficult to achieve. As for the functionality of the technical system,there is no common way for its measuring and quantification. Thisis the reason why in this paper, in order to assess the effectiveness,expertise judgments of the employed in the working process of theanalyzedmachineswillbeused.Applicationofexpertisejudgments has been largely used in literature, primarily for dataprocessing and the assessment of the technical systems in termsof: risk (Li & Liao, 2007), safety (Wang 2000; Wang, Yang, & Sen,1995) or dependability (Ivezic et al., 2008; Tanasijevic, Ivezic,Ignjatovic, & Polovina, 2011). Expertise judgment is naturally givenin linguistic form. Thereby, as the logical mathematical andconceptual model for processing the expertise judgments, namelyfor calculating with linguistic descriptions, the fuzzy set theorywas used (Klir & Yuan, 1995; Zadeh, 1996). Application of fuzzysets today represents one of the most frequently used tools forsolving the problems in various areas of optimization (Huang,Gu, & Du, 2006) andidentification (Chan, 1996) regardingengineering problems. Cai (1996) presents the overview of variousapplication aspects of fuzzy methodology in systemfailureengineering, which is a problem close to effectiveness assessment.Application of fuzzy logic theory and experts systems (Liao,2011; Liebowitz, 1988) in general is also used for solving theoptimizations problems from area of agro machinery. In article(Rohani, Abbaspour-Fard, & Abdolahpour, 2011) on the base ofneural networks application, failures on tractors were predicted.In article (Ye, Yu, & Zhao, 2010) fuzzy mathematics, reliabilitytheory and multi-objective optimization technology were appliedto design tractors final transmission. Predictability of machinedowntimes and reliability, significantly depends on its effective-ness of technical systems.The idea of this paper is to establish the model for effectivenessdetermination according to fuzzy sets theory utilization. Therebythe fuzzy sets were used to analyze reliability, maintainabilityand functionality performances (partial indicators of effectiveness)as well as for their integration into effectiveness. In this way effec-tive model for the quality assessment of the technical systems intheir working conditions is obtained. The model can be used as cri-teria for decision making related to any procedure in purchasing,operation or maintenance of the system, for prediction of repairand maintenance costs. Quality and functionality of the proposedmodel is shown in effectiveness determination of agriculturalmachinery, precisely tractors.2. Effectiveness performance assessment based on fuzzy setstheoryMathematical and conceptual model of effectiveness assess-ment is practically summarized in two steps: fuzzy propositionof effectiveness partial indicators; and fuzzy composition of men-tioned indicators into one synthesized. Fuzzy proposition is pro-cedure for representing the statement that includes linguisticvariables based on available information about considered techni-cal system. In that sense it is necessary to define the names of lin-guistic variables that represent different grades of effectiveness ofconsidered technical system and define the fuzzy sets that describethe mentioned variables. Composition is a model that providesstructure of indicators influences to the effectiveness performance.2.1. Fuzzy model of problem solvingThe first step in the creation of fuzzy model for effectiveness (E)assessment is defining linguistic variables related to itself and toreliability (R), maintainability (M) and functionality (F). Regardingnumber of linguistic variables, it can be found that seven is themaximal number of rationally recognizable expressions that hu-man can simultaneously identify (Wang et al., 1995). However,for identification of considered characteristics even the smallernumber of variables can be useful because flexibility of fuzzy setsto include transition phenomena as experts judgments commonlyis (Ivezic et al., 2008). According to the above, five linguistic vari-ables for representing effectiveness performances are included:poor, adequate, average, good and excellent. Form of these linguis-tic variables is given as appropriate triangular fuzzy sets (Klir &Yuan, 1995), and they are presented in Fig. 2.In Fig. 2, j = 1,.,5 practically represents measurement units ofeffectiveness.Thereby, partial indicators of effectiveness: R, M and F, pre-sented as membership functionl:lR l1R;.;l5R;lM l1M;.;l5M;lF l1F;.;l5F1In the next step, maxmin composition is performed on them. Maxmin composition, also called pessimistic, is often used in fuzzy alge-bra as a synthesis model (Ivezic et al., 2008; Tanasijevic et al., 2011;Wang et al., 1995; Wang 2000). The idea is to make overall assess-ment (E) equal to the partial virtual representative assessment. Thisassessment is identified as the best possible one between the worstpartial grades expected (R, M or F).It can be concluded that all elements of (R, M and F) that makethe E have equal influence on E, so that maxmin composition willbe used, which in parallel way treats the partial ones onto theFig. 1. Time picture of state, t time spent in operation,s time in failure, h time of planned shut down due to preventive maintenance.R. Miodragovic et al./Expert Systems with Applications 39 (2012) 894089468941synthetic indicator. In literature (Ivezic et al., 2008; Wang et al.,1995) maxmin compositions which by using operators ANDand OR provide an advantage to certain elements over the othersin the process of synthesis, are also used.Precisely, if we look at three partial indicators, namely theirmembership function (1), it is possible to make C = j3= 53combina-tions of their membership functions. Each of these combinationsrepresents one possible synthesis effectiveness assessment (E).E lj1;.;5R;lj1;.;5M;.;lj1;2;.5Fhi;for all c 1 to C2If we take into account only values iflj1;.;5R;M;F 0, we get combina-tions that are named outcomes (o = 1 to O, where O # C).Further, for each outcome its values are calculated (Xc). Theoutcome which would suit the combination c, it would be calcu-lated following the equations:XcPR;M;Ejhic33Finally, all of these outcomes are treated with maxmin composi-tion, as follows:(i) For each outcome search for the MINimum value oflR,M,Finvector Ec(2). The minimum which would suit the combina-tion o, it would be calculated following the equations:MIN0 minflj1;.;5R;lj1;.;5M.;lj1;.;5Fg;for all o 1 to O4(ii) Outcomes are grouped according to their valuesXc(3),namely the size of j.(iii) Find the MAXimum between previously identified mini-mums (i) for each group (ii) of outcomes. The maximumwhich would suit value of j, would be calculated followingthe equations:MAXj maxfMINog; for every j5E assessment of technical system is obtained in the form:lE MAXj1;.;MAXj5 l1E;.;l5E6This expression (6) is necessary to map back to the E fuzzy sets(Fig. 2). Best-fit (Wang et al., 1995), method is used for transforma-tion of E description (6) to form that defines grade of membershipto fuzzy sets: poor, adequate, average, good and excellent. This pro-cedure is recognized as identification. Best-fit method uses distance(d) between E obtained by maxmin composition (6) and each ofthe E expressions (according to Fig. 2), to represent the degree towhich E is confirmed to each of fuzzy sets of effectiveness (Fig. 2).diEj;Hi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX5j1ljE ?ljHj2vuut;j 1;.;5;Hi fexcellent;goodaverage;adequate;poorg7where is (according to Fig. 2):lexc.= (0,0,0,0.25,1);lgood= (0,0,0.25,1,0.25);laver.= (0,0.25,1,0.25,0);ladeq.= (0.25,1,0.25,0,0);lpoor= (1,0.25,0,0,0).The closerlE(6) is to the ith linguistic variable, the smaller diis.Distance diis equal to zero, iflE(6) is just the same as the ithexpression in terms of the membership functions. In such a case,E should not be evaluated to other expressions at all, due to theexclusiveness of these expressions.Suppose dimin(i = 1,.,5) is the smallest among the obtaineddistances for Ejand leta1,.,a5represent the reciprocals of the rel-ative distances (which is calculated as the ratio between corres-ponding distance di(7) and the mentioned values dimin). Then,aican be defined as follows:ai1di=dimin;i 1;.;58If di= 0 it follows thatai= 1 and the others are equal to zero. Then,aican be normalized by:biajP5m1aim;i 1;.;5X5i1bi 19Each birepresents the extent to which E belongs to the ith defined Eexpressions. It can be noted that if Eicompletely belongs to the ithexpression then biis equal to 1 and the others are equal to 0. Thus bjcould be viewed as a degree of confidence that Eibelongs to the ith Eexpressions. Final expression for E performance at the level of tech-nical system, have been obtained in the form (10)Eifbi1;poor;bi2;adequate;bi3;good;bi4;average;bi5;excellentg103. An illustrative exampleAs an illustrative example of evaluation of agriculture machin-ery effectiveness, the comparative analysis of three tractors A1B2,and C2is given in this article.In tractor A a 7.146 l engine LO4V TCD 2013 is installed. Thanksto the reserves of torque from 35%, the tractor is able to meet allthe requirements expected in the worst performing farming oper-ations in agriculture. Total tractor mass is 16,000 kg. According toOECD (CODE II) report maximum power measured at the PTO shaftis243 kWat2200 rpmwithspecificfuelconsumptionof198 g/kW h (ECE-R24). Maximum engine torque is 1482 Nm at en-gine regime of 1450 rpm. Transmission gear is vario continioustransmision. Linkage mechanism is a Category II/III with liftingforce of 11,800 daN.In tractors B2and C28.134 l engine 6081HRW37 JD is installed,with reserve torque of 40%, and this tractor was able to meet all therequirements expected in the worst performance of the farmingoperations in agriculture. Total tractor weight is 14,000 kg. Accord-ing to OECD (CODE II) report maximum power measured at thePTO shaft is 217 kW at 2002 rpm with specific fuel consumptionof 193 g/kW h (ECE-R24). Maximum torque is 1320 Nm at enginerevs of 1400 rpm. Transmission is AutoPower. Linkage mechanismis a Category II/III with lifting force of 10,790 daN.Both models have electronically controlled tractor engine andfuel supply system that meets the regulations on emissions.From the submitted technical characteristics of the tractor A, Band C it is seen that all three tractors are fully functional forFig. 2. Effectiveness fuzzy sets.1Tractor Fendt Vario 936.2Tractor John Deere 8520.8942R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946performing difficult operations for different technologies of agri-cultural production. Tractors B and C have the same technical char-acteristics, and practice is the same type and model, except thatthe tractor B entered into operation in May 2007, a tractor C in June2007. A tractor on the experimental farm, which is the technicaldocumentation for the base model, comes into operation in July2009. The main task of maintaining agricultural techniques is toprovide functionality and reliability of machines. Maintenance ofall three tractors is done by machine shop owned by the user up-grade option.Ten engineers (analysts) working on maintenance and opera-tion of tractors were interviewed. Their evaluation of R, D and Fare given in Table 1.First, the effectiveness of tractor A is calculated. It can be seenthat the reliability was assessed as excellent by six out of ten ana-lysts (6/10 = 0.6), as average by three (0.3) and as good by one(0.1). In this way the assessment R is obtained in the form (11):R 0:6=exc; 0:3=good; 0:1=aver; 0=adeq; 0=poor11In the same way the assessments for M and F are obtained:M 0:4=exc; 0:4=good; 0:2=aver; 0=adeq; 0=poorF 0:5=exc; 0:5=good; 0=aver; 0=adeq; 0=poorIn the next step, these assessments are mapped on fuzzy sets (Fig. 1)in order to obtain assessment in the form (1). For example, Reliabil-ity in this example is determined as (11), where it is to linguisticvariable excellent joined weight 0.6. Thereby, fuzzy set excellentis defined as: Rexc= (1/0, 2/0, 3/0, 4/0.25, 5/1.0) (according toFig. 1). In this way the specific values of fuzzy set excellentRexc0.6= (1/(0 ? 0.6),2/(0 ? 0.6),3/(0 ? 0.6),4/(0.25 ? 0.6),5/(1.0 ? 0.6) are obtained. The remaining four linguistic variablesare treated in the same way. In the end for each j = 1,.,5 specificmembership functions (last row, Table 2) are added into the finalfuzzy form (1) of tractor A reliability:lRA 0;0:025;0:175;0:475;0:675In the same way, based on the questionnaire (Table 1) values formaintainability and functionality are obtained:lMA 0;0:05;0:3;0:55;0:5;lFA 0;0;0:125;0:625;0:62512These fuzzificated assessments (11) and (12) are necessary to syn-thesize into assessment of effectiveness, using maxmin logics. Inthis case it is possible to make C = 53= 125 combinations, out ofwhich the 48 outcomes. First outcome would be for combination2-2-3: E2-2-3= 0.025,0.05,0.125, where isX2-2-3= (2 + 2 + 3)/3 = 2(rounded as integer). Smallest value among the membership func-tions of this outcome is 0.025. Other outcomes and correspondingvalues ofXcare shown in Table 3. All these outcomes can begrouped around sizesX= 2, 3, 4 and 5.For example, for outcomeX= 5 it can be written:E4?5?5 0:475;0:5;0:625?;E5?4?5 0:675;0:55;0:625?;E5?5?4 0:675;0:5;0:625?;E5?5?5 0:675;0:5;0:625?Further, for each of them, minimum between membership functionis sought:Table 1Results of questionnaire.AnalystLinguistic variablesTractor ATractor BTractor CExcellentGoodAverageAdequatePoorExcellentGoodAverageAdequate