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专家系统与应用程序基于模糊集理论的有效性的评估农业机械摘要:农业机械生产的服务质量是代表农业成功的基本因素之一。从这个意义上说,有一个明确需要定义这些机器质量的具体指标,它有可能决定哪些机器适合不同工作条件。服务的技术系统概念的有效性代表质量的一个综合指标。本文运用模糊集理论定义的有效性和可靠性、可维护性和功能作为影响指标的有效性。在这个意义上的模型评估的有效性拖拉机作为农业的典型代表机器已经形成。本模型是基于集成上述的语言描述。利用模糊集理论和max-min成分影响指标,模型进行了测试。同一类别的三个拖拉机为例,利用的气候和土壤条件在更广泛的贝尔格莱德(塞尔维亚)地区。即使在这个实验中条件是非常重要参数 , 相比于其他操作,实现的效果差异也达到大致相等。1.介绍为达到扩张的全球农产品的要求,实现更大的农业技术的发展。人们普遍认识到当代农业系统中需要适当的机器和设备,仔细和详细规划的需求和控制所有相关的生物、技术、技术和其他进程。最终结果的准确、可靠的预测为每个指定的操作,以及完整的作物生产过程中,。要求加强了引入复杂的实验,数学,农业科学统计,机械和其他方法都是特别重要的。在过去的几十年。除了上述的要求,一个适当的技术体系必须满足生产力的标准,期望的作物生产。在大多数情况下,在塞尔维亚,tractor-machinery农场系统的能力远远超过最优级别(尼克里奇,2005),增加成本作物生产。目前,现有的数学优化方法、支持的高性能计算机有效地解决优化问题(Dette &韦伯达菲et al .,1990;1994;Mileusnic,2007;等等)。一个最优的技术体系的形成为我们生产了更便宜的食品,高度影响拖拉机的可靠性、可维护性和系统的功能。与系统科学发展同样,实际上的开始是IIWorld战争后,在适当的工程和科学文献定义了一系列的概念,来描述技术系统的基本特征的点的服务质量。可靠性的指标是技术系统和行为操作,技术指标和可维护性systembehaviors期间的失败可以表示为大多数可辨认的概念。这两个概念及其实现最先进的发展。有效性的概念被定义在试图描述同时技术操作系统的行为和失败的时期。这概念考虑可靠性和可用性的表演,以及提出了技术系统设计的功能(Papic&Milovanovic2007)。换句话说,一个技术系统的有效性的概率,一个成功的功能系统技术和执行所需的准则函数限制允许的差异对于给定时间和给定的周围条件。虽然在相同的精神,一些作者定义有效性有所不同。在(Ebramhimipour &铃木,2006)被定义为总体有效性的指标包含效率、可靠性和可用性。这两个引用定义包括并行关于可靠性和可用性,虽然可用性包括可靠性和可维护性(Ivezic,Tanasijevic,& Ignjatovic,2008)。因此它可以商定有效性是影响可靠性、可维护性的功能。可靠性系统不断的被定义为特征保持操作abilitywithin允许的差异极限在现在;可维护性的能力是预防和发现故障及损坏,系统更新通过参加技术和操作能力和功能维修,功能实现功能的程度要求,即调整环境,或更准确系统运行的条件。监测的可靠性和可维护性是常见的监控时间的状态显示(图1)可靠性和可维护性的函数可以确定,以及操作的平均时间和平均时间相关。主要问题出现在形成时间的照片数据监控和记录。在现实条件的机器应该连接到信息系统将准确记录每一个失败、持续时间和修复程序。这通常是昂贵或简易监测机器的性能,即关闭的,是不精确的。此外,提供的统计数据处理时间的状态要求所有的机器在平等的条件下工作,这是难以实现。至于技术体系的功能,没有共同的方法测量和量化。这在本文的原因,为了评估的有效性, 将使用专业知识和分析机器判断工作的工作过程。应用专业知识判断主要用于文学,主要是为数据处理和评估的技术系统而言:风险(Li 廖,2007)、安全(王2000;王、杨、&森1995)或可靠性,用专业知识判断自然的语言形式。因此,数学和逻辑概念模型进行处理的经验判断,即计算的语言描述,模糊集合理论使用(Klir &元,1995;枝,1996)。应用模糊今天集代表了最常用的工具之一各领域解决问题的优化(黄顾,&杜,2006)和识别(陈,1996)过程问题。Cai(1996)提出了不同的概述应用程序方面的模糊方法在系统失败工程,这是一个接近效能评估问题。应用模糊逻辑理论和专家系统(辽、一般2011;Liebowitz,1988)也用于解决优化问题的农业机械领域。(Abbaspour-Fard Rohani & Abdolahpour,2011)的基础上神经网络的应用程序,在拖拉机预测失败。(Yu,你们&赵,2010)模糊数学、可靠性理论和多目标优化技术应用设计拖拉机最终传动。机器的可预测性和可靠性,显著依赖于其有效性的技术系统。本文的观点是根据模糊集理论的利用率建立模型的有效性。从而说明模糊集是用于分析可靠性、可维护性和功能表现(部分指标的有效性)以及为他们融入效率。他们的工作是以这种有效模型质量的方式评估技术系统。模型可以作为标准购买决策相关的任何程序,系统的操作或维护,修理的预测和维护成本。质量和功能的建议模型有效性的确定农业所示机械、拖拉机。2。基于模糊集的有效性表现评估理论数学和概念模型的有效性评估实际上是在两个步骤:总结模糊命题的部分的效性指标;模糊提到的分成一个指标合成。模糊命题过程为代表的声明,包括语言变量基于可用的信息技术系统。在这个意义上它必须定义语言的名字变量,代表不同的等级的效果考虑技术系统和定义的模糊集描述提到的变量。作文是一个模型,它提供了影响结构有效性性能的指标。2.1。模糊模型解决问题第一步创建的模糊有效性模型(E)评估本身和定义语言变量以及可靠性(R)、可维护性(M)和功能(F)有关.许多语言变量,它可以发现最大数量的理性,人类可辨认的表达式可以同时识别(王et al .,1995)。然而,识别的考虑甚至较小的特征数量的变量可以有用,因为专家的判断(Ivezicet al .,2008)模糊集的灵活性一般包括过渡现象。根据以上,五个语言变量为代表的有效性表现包括:穷,充足,平均,和优秀。这些语言形式变量给出适当的三角模糊集(Klir 元,1995),图2所示。在图2中,j = 1,。实际上,5代表的计量单位有效性。因此,部分指标的有效性:R、M和F,隶属函数l:在下一步中,执行max-min组成。马克斯-敏成分,也称为悲观,经常用于模糊代数作为一个综合模型(Ivezicet al .,2008;Tanasijevic et al .,2011;王王et al .,1995;2000)。这个想法是为了让整体评估(E)等于部分虚拟代表评估。这评估被确定为之间的最好的一个最坏的打算部分成绩(R、M或F)。它可以得出的结论是,所有的元素(R、M和F)E有同等影响E,max-min组成以并行方式被使用,这将部分的到综合指标。在文学(Ivezicet al .,2008;etal .,1995)max-min成分通过运营商”和“和”或“提供一个优势在其他的某些元素在合成的过程中,也使用。准确地说,如果我们看看三个部分指标,即他们的隶属函数(1),可以使C:= j3 = 53组合的隶属度函数。每一种组合代表一个可能的合成效果评估(E)。这个表达式(6)有必要映射回E模糊集(图2)。最佳(王et al .,1995),用于转换方法E描述(6)形成定义等级的会员模糊集:贫穷、充足,平均,和优秀的好。这个过程被公认为识别。最佳方法是使用距离E(d)之间通过“max-min”成分(6)和每个人E表达式(根据图2)来表示的程度E是确认每个模糊集的有效性(图2)。越接近勒(6)是第i个语言变量,小迪。距离di等于零,如果勒(6)只是第i个相同隶属度函数的表达式。在这种情况下,E不应该评估其他表达式,由于这些表达式的排他性。假设迪民(i = 1,。,5)是最小的距离对Ej,让a1,。,a5代表相对的倒数距离(计算相应的比率距离di(7)和迪民提到的值)。然后,人工智能:1.一个说明性的例子作为一个说明性的例子对农业机械的评价有效性,比较分析三个拖拉机A1 B2、本文给出和C2。在拖拉机7.146 l发动机LO4V TCD 2013安装。谢谢从35%的扭矩储备,拖拉机是能够满足所有需求预期表现最差的农业操作在农业。总拖拉机质量是16000公斤。根据经济合作与发展组织(代码2)报告最大动力输出轴功率测量在2200转243千瓦的燃油消耗率吗198 g /千瓦小时(ECE-R24)。发动机的最大扭矩1482海里在引擎1450 rpm的政权。传动装置是精心“不一样的”传达。事业联动机制是一个类别II / III与提升11800公斤。在拖拉机B2和C2 8.134 l发动机6081 hrw37 JD安装,储备扭矩的40%,这能够满足所有的拖拉机需求预期表现最差的农业在农业操作。拖拉机总重量是14000公斤。根据经合组织(代码2)报告最大的权力来衡量动力输出轴在2002转217千瓦燃料消耗率193克/千瓦小时(ECE-R24)。在发动机最大扭矩1320海里转速为1400 rpm。传播是“AutoPower。联动机制是一个类别II / III 10790丹的提升力。两个模型都是电子控制拖拉机发动机和燃料供给系统,满足排放法规。从提交的技术特点的拖拉机,B和C看到所有三个拖拉机全功能forperforming困难操作不同的农业技术生产。拖拉机B和C有相同的技术特征,和实践是相同的类型和模式,除了拖拉机B进入操作在2007年5月,一辆拖拉机C 6月2007年。一辆拖拉机实验农场,这是技术文档的基本模型,在7月份进入操作2009年。保持农业技术的主要任务提供功能和机器的可靠性。维护所有三个拖拉机是通过机器商店所拥有的用户升级选择。十个工程师(分析师)致力于维护和操作拖拉机的采访。他们评价R,D和F表1中给出。首先,拖拉机是计算的有效性。可以看出可靠性是由十的分析师评为优秀(6/10 = 0.6),平均三(0.3)和一样好(0.1)。以这种方式获得评估R在表单中,在下一步中,这些评估是映射在模糊集(图1)为了获得评估(1)。例如,可靠性在这个例子中确定(11),它是语言0.6变量优秀加入重量。因此,模糊集优秀定义为:Rexc=(1/0,1/0,1/0,4/0.25 5/1.0)(据吗图1)。这样的特定的值模糊集优秀Rexc0.6 =(1 / 0.6(0),2 / 0.6(0),3 / 0.6(0),4 /(0.25 - 0.6),5 /(1.0 - 0.6)。剩下的四个语言变量被以同样的方式对待。最后对于每个j = 1,。5具体隶属度函数(最后一行,表2)被添加到最后拖拉机可靠性模糊形式(1):这些fuzzificated评估(11)和(12)是合成所必需的评估的有效性,使用max-min逻辑。在这种情况下可以使C = 53 = 125组合,走出48的结果。第一个结果是组合2-2-3:E2-2-3(0.025,0.05,0.125),哪里X2-2-3 =(2 + 2 + 3)/ 3 = 2(四舍五入为整数)。最小值的隶属度函数这一结果的是0.025。其他的结果和相应的我的值如表3所示。所有这些结果都可以围绕尺寸X = 2、3、4和5。拖拉机在很大程度上为0.30065(与30%)评估那么好,拖拉机在很大程度上0.27538(27.5%)评估一样好,而拖拉机C在很大程度上为0.25468平均(25.5%)评估。它可以得出的结论是,C是最糟糕的,当拖拉机只是稍微比B,特别是如果我们看到的评估为优秀的28.8%,而B的程度23.8%的程度。分析了拖拉机可以提出的有效性如图3。,它可以更清楚地看到,拖拉机的最大的效果。如果这个评估(EA,EB,EC)defuzzificated是重心点计算- Z(Bowles & Pelaez,1995),我们得到了评估的效果如下:这就意味着在1 - 5(即从贫困的规模优秀)拖拉机是最好的和拖拉机C是最坏的打算。验证的实现结果,统计分析的可用性,像家庭与有效性概念,已经被使用。那在我们的模型显示,拖拉机是最好的,和C的坏的效果。在现实中,如果我们分析的可用性,它是看到2904 moto-hours拖拉机在工作3130年可用moto-hours;如果10000 moto-hours计算,在9244年的工作将花费moto-hours。拖拉机B的10004年moto-hours可用,它花9069moto-hours在工作,和拖拉机C 9981可用moto-hours花了9045年的工作。实验表明,更可靠和有效的拖拉机是少是延迟。在某种程度上,这个初始的优势消灭更糟糕的物流交付备件的时候涉及到拖拉机,拖拉机a . 1100年moto-hours工作可怜的物流在维护希望8个工作日, 一个给定的拖拉机和它极大地影响了可维护性的下降带来的好处,因此相同的效率(内部技术PKB)总剥削的下降。1.结论本文提出一种模型有效性的评估技术系统、精确农业机械、基于模糊集理论。表现作为整体的有效性指标系统的服务质量,即为整个测量技术系统的可用性。可靠性、可维护性和功能表演已经公认的有效性参数或指标。语言可以被任命为形式所有提到的共同特征指标。因此模糊集理论出现自然工具建模的有效性。在本文中,应用模糊集理论,这是必要的定义:语言变量及其描述隶属函数、模糊规则的组成和模型集成和去模糊化。模糊的成分即max-min逻辑已经被用于集成的有效性指标有效性的整体性能,最适合集成的方法模糊集的隶属函数和质心点去模糊化的模糊数的计算数值。Max-min组合模型,它暴露在这篇文章中,没有以这种方式处理相应的文献。另外,在案例研究中,模型的模糊化的问卷调查的结果,它代表的正是所积累的方式工程师的知识和技能。提出的模型可以作为一个简单的工具的快速估计的有效性即为农业服务的质量机械、基于专家判断和估计。在同时,该模型不需要复杂的IT基础设施。分析实现模糊集和适当的模糊有效性可靠性、可维护性和功能表现可以纠正措施的指导购买的方向吗的设备,结构调整,改变的维护政策或管理/运营商变更本文具体分析了三个拖拉机,标志着一个B和C,这表明更高效的拖拉机越频繁宕机。在某种程度上,这种最初的优势就终止了穷交付备件物流。感谢研究工作得到了塞尔维亚共和国教育部和科学界的支持。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: /locate/eswaduring 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 CExcellentGoodAverageAdequatePoorExcellentGoodAverageAdequatePoorExcellentGoodAverageAdequatePoor1RxxxMxxxFxxx2RxxxMxxxFxxx3RxxxMxxxFxxx4RxxxMxxxFxxx5RxxxMxxxFxxx6RxxxMxxxFxxx7RxxxMxxxFxxx8RxxxMxxxFxxx9RxxxMxxxFxxx10RxxxMxxxFxxxR. Miodragovic et al./Expert Systems with Applications 39 (2012) 894089468943MINE4?5?5 minf0:475;0:5;0:625g 0:475;MINE5?4?5 0:55;MINE5?5?4 0:5;MINE5?5?5 0:5Between these minimums, in the end it seeks maximum:MAXXd5 maxf0:475;0:55;0:5;0:5g 0:55Also for other values:X: MAXX= 2= 0.025; MAXX= 3= 0.175;MAXX= 4= 0.55 (Table 1.)Finally, we get expression for membership function of effective-ness of tractor A:lEA 0;0:025;0:175;0:55;0:55Best-fit method (79) and proposed E fuzzy set (Fig. 1) give the finaleffectiveness assessment for the tractor A:d1E;excffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX5j1ljE?ljexc2vuutffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0?020:025?020:175?020:55?0:2520:55?12q0:56899where is :lE 0;0:025;0:175;0:55;0:55lexc 0;0;0;0:25;1Forotherfuzzysets:d2(E,good) = 0.54658,d3(E,aver) = 1.06007, d4(E, adeq) = 1.27426, d5(E, poor) = 1.29856.for dmin d2:a11d1=d210:56899=0:54658 0:96061;a2 1:00000;a3 0:51561;a4 0:42894;a5 0:42091:b1a1P5i1ai0:969010:96901 1 0:51561 0:42894 0:42091 0:28881;b2 0:30065;b3 0:15502;b4 0:12896;b5 0:12655:Finally, we get the assessment of effectiveness of tractor A, inform (10):EA= (b1, excellent), (b2, good), (b3, average), (b4, ade-quate),(b5,poor) = (0.28881,excellent),(0.30065,good),(0.15502,average),(0.12896,adequate),(0.12655, poor)In the same way, we get the assessments for other two tractorsB and C:EB= (0.23793, excellent), (0.27538, good), (0.20635, aver-age), (0.14693, adequate), (0.13342, poor)EC= (0.17507, excellent), (0.25092, good), (0.25468, aver-age), (0.17633, adequate), (0.14300, poor).Tractor A is in great extent of 0.30065 (in relation to 30 %) as-sessed as good, tractor B in great extent of 0.27538 (27.5%) as-sessed as good, while tractor C is in great extent of 0.25468(25.5%) assessed as average. It can be concluded that C is the worst,while tractor A is only somewhat better than B, especially if we seeTable 2Calculation of specific values of fuzzy sets.123450.6/exc.0 ? 0.60 ? 0.60 ? 0.60.25 ? 0.61.0 ? 0.60.3/good0 ? 0.30 ? 0.30.25 ? 0.31.0 ? 0.30.25 ? 0.30.1/aver.0 ? 0.10.25 ? 0.11.0 ? 0.10.25 ? 0.10 ? 0.10/adeq.0.25 ? 01.0 ? 00.25 ? 00 ? 00 ? 00/poor1.0 ? 00.25 ? 00 ? 00 ? 00 ? 0PR00.0250.1750.4750.675Table 3Structure of MAXMIN composition.Comb.XlMIN23452-2-320.025,0.05,0.1250.0252-2-430.025,0.05,0.6250.0252-2-530.025,0.05,0.6250.0252-3-330.025,0.3,0.1250.0252-3-430.025,0.3,0.6250.0252-3-530.025,0.3,0.6250.0252-4-330.025,0.55,0.1250.0252-4-430.025,0.55,0.6250.0252-4-540.025,0.55,0.6250.0252-5-330.025,0.5,0.1250.0252-5-440.025,0.5,0.6250.0252-5-540.025,0.5,0.6250.0253-2-330.175,0.05,0.1250.053-2-430.175,0.05,0.6250.053-2-530.175,0.05,0.6250.053-3-330.175,0.3,0.1250.1253-3-430.175,0.3,0.6250.1753-3-540.175,0.3,0.62500.1753-4-330.175,0.55,0.1250.1253-4-440.175,0.55,0.6250.1753-4-540.175,0.55,0.6250.1753-5-340.175,0.5,0.1250.1253-5-440.175,0.5,0.6250.1753-5-540.175,0.5,0.6250.1754-2-330.475,0.05,0.1250.054-2-430.475,0.05,0.6250.054-2-540.475,0.05,0.6250.054-3-330.475,0.3,0.1250.1254-3-440.475,0.3,0.6250.34-3-540.475,0.3,0.6250.34-4-340.475,0.55,0.1250.1254-4-440.475,0.55,0.6250.4754-4-540.475,0.55,0.6250.4754-5-340.475,0.5,0.1250.1254-5-440.475,0.5,0.6250.4754-5-550.475,0.5,0.6250.4755-2-330.675,0.05,0.1250.055-2-440.675,0.05,0.6250.055-2-540.675,0.05,0.6250.055-3-340.675,0.3,0.1250.1255-3-440.675,0.3,0.6250.35-3-540.675,0.3,0.6250.35-4-340.675,0.55,0.1250.1255-4-440.675,0.55,0.6250.555-4-550.675,0.55,0.6250.555-5-340.675,0.5,0.1250.1255-5-450.675,0.5,0.6250.55-5-550.675,0.5,0.6250.5MAX0.0250.1750.550.558944R. Miodragovic et al./Expert Systems with Applications 39 (2012) 89408946that A is assessed as excellent in the extent of 28.8% while B in theextent of 23.8%. Effectiveness of analyzed tractors can be presentedas in Fig. 3., where it can be more clearly seen that tractor A has thebiggest effectiveness.If this assessment (EA, EB, EC) is defuzzificated by center of masspoint calculation Z (Bowles & Pelaez, 1995), we get the assess-ment of effectiveness as follows:ZAP5i1bi?CiP5i1bi0:28881?50:30065?40:15502?30:12896?20:12655?10:288810:300650:155020:128960:126553:50 ZB3:34 and ZC3:14where C is numerical equivalent for linguistic variables (poor = 1,adequate = 2.)This would mean that on the scale of 15 (i.e. from poor toexcellent) tractor A is the best and tractor C is the worst.For verification of achieved results, statistical analysis of avail-ability, like family concept with effectiveness, has been used. Thatis, in our model showed that the tractor A is of a best, and C ofworst effectiveness. In reality, if we analyze the availability, it isseen that the tractor A spent in work 2904 moto-hours, outof 3130 available moto-hours; if calculated on 10,000 moto-hours,it would spend in work 9244 moto-hours. As of the tractor B, out of10,004 available moto-hours, it spent 9069 moto-hours in work,and tractor C out of 9981 available moto-hours spent 9045 in work.The experiment showed that the more reliable and efficienttractors are the less frequent are delays. In part, this initial advan-tage wiped out worse logistics of delivery of spare parts when itcomes to tractor A. in 1100 moto-hours work of the tractor, dueto poor logistics in maintaining hoped to eight working days, andit greatly influenced the decline in benefits of maintainability ofa given tractor and thus the decline in total exploitation of thesame efficiency (Internal technical documentation PKB).4. ConclusionThis paper presents a model for effectiveness assessment oftechnical systems, precisely agricultural machinery, based on fuzzysets theory. Effectiveness performance has been adopted as overallindicator of systems quality of service, i.e. as entire measure oftechnical system availability. Reliability, maintainability and func-tionality performances have been recognized as effectivenessparameters or indicators. Linguistic form can be appointed as thecommon characteristics of all mentioned indicators. Therefore fuz-zy sets theory has appeared as natural tool for effectiveness mod-eling. In this article, for application of fuzzy set theory, it wasnecessary to define: linguistic variables and their description bya membership function, rules of fuzzy composition and models ofintegration and defuzzification. Fuzzy composition i.e. maxminlogic has been used for integration of effectiveness indicators inthe overall effectiveness performance, best fit method for integra-tion of membership function in fuzzy set and center of mass pointcalculation for defuzzification of fuzzy number in numeric values.Maxmin composition model, which is exposed in this paper, hasnot been processed in this way in corresponding literatures. Also,in case study, the model of fuzzification of results of questionnaireis presented, which represent precisely shown way of accumula-tion of engineer knowledge and expertise.Presented model can be used as a simple tool for the fast esti-mation of effectiveness i.e. quality of service for agriculturalmachinery, based on experts judgments and estimations. At thesame time, the model does not require a complex IT infrastructure.Analysis of achieved effectiveness fuzzy sets and appropriate fuzzysets for reliability, maintainability and functionality performancescan be the guideline for corrective actions in the directions of pur-chase of equipment, construction adjustment, changing of mainte-nance policy or management/operators alteration.The paper specifically analyzes the three tractors, marked A, Band C, which showed that the more efficient tractors the less fre-quent downtimes. In part, this initial advantage is annulled poorerdelivery of spare parts logistics.AcknowledgementThe research work is supported by
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本文标题:自走式大蒜收获机设计【含三维UG模型】【含9张CAD图纸和文档资料】
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