摘 要
單株脫粒機是農(nóng)作物科研育種工作重要環(huán)節(jié)，機械脫粒可以提高工作效率，減少科研勞動強度，節(jié)約人工成本。目前很多作物已經(jīng)發(fā)明了專用的單株脫粒機，例如小麥、水稻、玉米等。而農(nóng)作物單株脫粒研制在我國還是空白，多數(shù)科研單位主要還是靠人工捶打或者手工剝莢進(jìn)行單株脫粒。國外已經(jīng)發(fā)明了農(nóng)作物單株脫粒機，但是進(jìn)口農(nóng)作物單株脫粒機價格昂貴，維修需要專門的進(jìn)口配件，零部件轉(zhuǎn)運周期長，且存在各種不足之處。近年來，國內(nèi)通過借鑒和改造，設(shè)計出的農(nóng)作物單株脫粒機，或多或少存在清機不徹底、易混雜和破碎率高等問題。單株脫粒要求機械脫粒要速度快，損失小，籽粒完好無損傷；清選是把雜質(zhì)去除，留下干凈籽粒，最重要是機器內(nèi)不能有籽粒夾雜現(xiàn)象。本設(shè)計的單株脫粒機具有操作安全可靠、效率高、不混雜、損失少、脫粒干凈、清潔度高等優(yōu)點。
關(guān)鍵詞：單株；農(nóng)作物；脫粒機；脫粒；設(shè)計
Abstract
Plot yield soybean thresher is important link in soybean research and breeding work， mechanical threshing can improve work efficiency and reduce the scientific labor intensity, saving labor costs. At present many crops have invented a special individual thresher, such as wheat, rice, corn, etc. And soybean plant threshing research is still blank in our country, most of the research unit is still mainly rely on artificial beat or manual peel pods per plant threshing. Abroad have invented the soybean single plant threshing, but imported soybean plant thresher is expensive, maintenance need special imported components and parts transfer cycle is long, and there are many shortcomings. In recent years, domestic by reference and renovation, design of soybean plant thresher, more or less exist cleaning machine, easy to mix with incomplete and broken rate is high. Plant threshing requirements to speed, mechanical threshing losses small, grain in good condition without damage; Cleaning is to remove impurities, leaving a clean the grain, the heaviest if the machine is the grain mixed phenomenon. This design of residential area of soybean thresher is safe and reliable operation, high efficiency, not mixed, less damage, threshing clean, high cleanliness, etc.
Keywords: Per plant; Soybean; Threshing Machine; Design
II
目 錄
摘 要 I
Abstract II
1 緒 論 1
1.1 設(shè)計的目的與意義 1
1.2 國內(nèi)外發(fā)展現(xiàn)狀 1
1.2.1 國外發(fā)展現(xiàn)狀 1
1.2.2 國內(nèi)發(fā)展現(xiàn)狀 2
2脫粒機的總體方案選擇分析及工作原理 3
2.1 總體方案的選擇 3
2.2 總體結(jié)構(gòu) 3
2.3 工作原理 4
3 電動機的選擇 5
3.1 電動機類型和結(jié)構(gòu) 5
3.2 選擇電動機的容量 5
3.3 選擇電動機的型號 5
4 帶傳動的設(shè)計 7
4.1 確定計算功率Pc 7
4.2 確定V帶型號 7
4.3 確定傳動比i 7
4.4 計算帶輪直徑 7
4.5 驗算帶速V 7
4.6 校核小帶輪的包角α1 8
4.7 計算所需V帶根數(shù)Z 8
5 螺旋輸送器（攪龍）的設(shè)計 9
5.1 攪龍的結(jié)構(gòu)型式 9
5.2攪龍葉片的螺旋角 9
5.3 攪龍的內(nèi)徑D1 10
5.4攪龍的外徑D2 10
5.5攪龍的螺距S 10
5.6攪龍的轉(zhuǎn)速n 10
5.7計算攪龍帶輪直徑 10
6 機架的設(shè)計 11
6.1 斷面形狀和尺寸選擇 11
6.2 結(jié)構(gòu)設(shè)計 11
7 主軸的設(shè)計和校核 12
7.1 選擇軸的材料 12
7.2 確定軸的直徑 12
7.3 軸的結(jié)構(gòu)設(shè)計 13
7.4 軸上零件的周向定位 13
7.5 滾筒主軸的強度校核 13
7.6 鍵連接的強度校核 15
8 軸承的選用 17
9 總 結(jié) 18
參考文獻(xiàn) 19
致 謝 20
1 緒 論
1.1 設(shè)計的目的與意義
隨著我國農(nóng)業(yè)的快速發(fā)展，我國越來越關(guān)注農(nóng)村問題，農(nóng)業(yè)機械化的發(fā)展速度進(jìn)一步加大，由機械代替人力作業(yè)已是我國農(nóng)業(yè)發(fā)展的一個趨勢，而要實現(xiàn)我國農(nóng)業(yè)的的現(xiàn)代化，其物質(zhì)承擔(dān)者農(nóng)業(yè)機具的開發(fā)與利用必須緊跟上；同時為了完成和鞏固本科知識和課程的學(xué)習(xí)；因此也就有了此時這篇設(shè)施作業(yè)機具全喂入式單株脫粒機的設(shè)計。
我國是一個以農(nóng)業(yè)生產(chǎn)為主的發(fā)展中國家，農(nóng)業(yè)的興衰與人們的生活，甚至國家的穩(wěn)定息息相關(guān)。但是現(xiàn)在隨著人口的增加和環(huán)境惡化，我國農(nóng)業(yè)發(fā)展也面臨著嚴(yán)峻考驗。如何讓在日常的生產(chǎn)影響中有效地提高生產(chǎn)率，實現(xiàn)一機多用是擺在人們面前的一個棘手的問題。實現(xiàn)農(nóng)業(yè)的現(xiàn)代化、智能化是今后農(nóng)業(yè)的必然選擇。
通過采用現(xiàn)代農(nóng)業(yè)工程和機械技術(shù)，為生產(chǎn)提供更多有利條件，并在某種程度上，有效地擺脫了對自然環(huán)境和農(nóng)業(yè)生產(chǎn)的依賴。它在人們的生活需求日益增長的同時發(fā)展起來，農(nóng)業(yè)生產(chǎn)是在人工可控制條件下，具有高投資、高技術(shù)含量、高質(zhì)量等特點。
近期生產(chǎn)收獲作業(yè)機具發(fā)展重點是：開發(fā)全喂入式單株脫粒機，合理選擇配套動力。體積和質(zhì)量小、動力足，操作舒適，減輕勞動強度是必然要求，動力最好選用電動機。
南方地區(qū)為了解決晚稻的肥料的問題，夏收的時侯有稻草回田當(dāng)綠肥用的習(xí)慣，這一點，全喂入單株脫粒機解決的比較好，當(dāng)在田間作業(yè)時，因脫粒后全部莖稈都被打碎了，有助于梨耕，莖稈也易于腐爛?？梢娮怨胖两瘢摿Ｉa(chǎn)對于農(nóng)業(yè)生產(chǎn)的重要性，因此在現(xiàn)階段對設(shè)施作業(yè)機具機構(gòu)的研究和設(shè)計是很有必要的。同時，脫粒機發(fā)展趨勢也成為各界關(guān)注的焦點。在這種情況下，有必要對我們國家的脫粒機發(fā)展現(xiàn)狀和未來發(fā)展趨勢等問題認(rèn)真研究，形成正確認(rèn)識，不僅對當(dāng)前我國軸流式脫粒相關(guān)行業(yè)技術(shù)進(jìn)步和產(chǎn)品定位具有重要意義，并對行業(yè)的未來發(fā)展有好處。
1.2 國內(nèi)外發(fā)展現(xiàn)狀
1.2.1 國外發(fā)展現(xiàn)狀
國外單株脫粒機的發(fā)展，基本上分為歐美和日本兩大類型。所謂歐美型，也就是說這些國家以旱地為主，地塊大，各類作物以小麥為主。而日本型是指以水田為主，大塊小，經(jīng)常規(guī)模也小，以水稻為主。因此，前者用的脫粒機是大型的，大功率的，而后者用的機型都是小型的或中型的。
雖然上述兩類地區(qū)因其自然條件不同，使用的機型不同，但其實現(xiàn)粒物收獲過程機械化所經(jīng)歷的過程卻大體上是相同的，即都是先從半機械化開始，然后逐步向機械化過渡，最后實現(xiàn)收獲過程機械化。到了五十年代，已基本上實現(xiàn)了收獲過程的半機械化。例如美國在1950年已擁有70萬左右的脫粒機，這時的機收面積已占收獲面積的75%?；旧蠈崿F(xiàn)了半機械化，到 了七十年代初美國脫粒機的數(shù)量達(dá)到85萬之多。機收面積達(dá)到了95%以上。正向大型、高效及自動化發(fā)展。
日本的情況有所不同，實現(xiàn)收獲過程半機械化的進(jìn)程要比歐美國家慢得多，當(dāng)然這里面有些客觀原因，地塊小，且是水田作業(yè)，因此到了六十年代的中期開始探索適用于日本的脫粒機。到了七十年代中期大約前后用了十年左右的時間，研制出多種適用于日本的脫粒機，脫粒機等產(chǎn)品。目前已大量推廣使用，可以說已基本上實現(xiàn)了收獲的過程半機械化。
從研究的動向看，著重于以下幾方面的研究：一是機具的可靠性；二是改善操作性能，提高自動化程度；三是使脫粒機更能適應(yīng)作物生長的自然條件，以提高脫粒機的適應(yīng)性、效率和工作質(zhì)量。四是提高勞動生產(chǎn)率。
1.2.2 國內(nèi)發(fā)展現(xiàn)狀
解放前，單株脫粒機也和其它農(nóng)業(yè)機械一樣是個空白，根本談不上研制。解放后，農(nóng)業(yè)機械化發(fā)展很快，單株脫粒機有了很大的發(fā)展，華北和東北不少地區(qū)已開始使用我國自己生產(chǎn)的脫粒機。近幾年我國已先后研制出幾種適用于北方地區(qū)使用的新機型，并已定型大批投產(chǎn)。我國南方十三個省市、區(qū)、市近年來大力發(fā)展了對脫粒機的研制，取得重大突破。機型都為全喂入式的，脫粒后不能保持莖稈完整，為了解決廣大農(nóng)民這一迫切要求，我國南方地區(qū)從1970年開始研制半喂入式脫粒機。經(jīng)過短短幾年努力，取得了重大成果，到目前為止先后經(jīng)省、區(qū)、市級定型的樣機已有十五種，形式多種多樣，有大有小。這些機型目前正大批投產(chǎn)，早定型投產(chǎn)的樣機，目前已在農(nóng)業(yè)生產(chǎn)上發(fā)揮了作用。
事物總是不斷發(fā)展的，單株脫粒機的研制工作也是一樣，還需要不斷的提高和發(fā)展；比如研究更為合理的新機型，實現(xiàn)標(biāo)準(zhǔn)化、系列化，并把新技術(shù)、新工藝和新材料應(yīng)用到脫粒機上，以便提高脫粒機的工作可靠性，改善其操作性能，減輕機器重量，提高其耐用度和降低造價。
2脫粒機的總體方案選擇分析及工作原理
2.1 總體方案的選擇
單株脫粒機采用的是全喂入型脫粒機構(gòu)，對于半喂入型脫粒機構(gòu)，其對作物的自然狀況比較敏感，生長亂的作物及高矮參差達(dá)的作物脫粒時，可能會造成無法脫凈。
單株脫粒機適于脫粒清選稻、麥及豆類、油菜、高粱、小米等多種農(nóng)作物的全喂入式單株脫粒機，屬于一種農(nóng)機設(shè)備。
總體方案確定的依據(jù)：1）完成農(nóng)作物的脫粒，保證脫盡率99%以上；2）要求農(nóng)作物破碎損失率不得大于0.5%；3）要求生產(chǎn)率高，機構(gòu)簡單，易維修，工作可靠；4）雜物盡量少；
2.2 總體結(jié)構(gòu)
該單株脫粒機主要由脫粒機、傳動變速裝置、滾筒蓋板、
凹板篩、螺旋推進(jìn)器、電動機、皮帶輪及機架等組成（見下圖）；1具有結(jié)構(gòu)緊湊，脫粒率高等優(yōu)點。
主要技術(shù)參數(shù)指標(biāo)：配用標(biāo)定功率12.1kw柴油機，標(biāo)定轉(zhuǎn)數(shù)2200r/min，整機凈質(zhì)量155kg。
1 機架 2 滑谷板 3 喂入口 4 導(dǎo)向板 5 殼蓋 6 釘齒式滾筒 7 排草口 8 凹板篩 9 螺旋推進(jìn)器 10 物料出料口
11 主動驅(qū)動皮帶輪 12 聯(lián)動皮帶輪 13 物料出口
2.3 工作原理
工作時，作物由進(jìn)口均勻進(jìn)入料機，高速運轉(zhuǎn)釘齒式滾筒把作物送入滾筒內(nèi)，作物一邊做圓周運動，一邊軸向運動，而凹板篩對運動物料產(chǎn)生一定阻力，使物料在滾筒的打擊下和凹板篩的搓揉下，實現(xiàn)脫離，經(jīng)過柵格式凹板篩篩孔實現(xiàn)精選。
3 電動機的選擇
3.1 電動機類型和結(jié)構(gòu)
電動機類型要由電源、工作條件、載荷特點和轉(zhuǎn)速來選擇。
由于本設(shè)計沒有特殊的要求，本設(shè)計電動機均從Y系列中選出。最終本設(shè)計選用Y系列三相異步電動機。
3.2 選擇電動機的容量
標(biāo)準(zhǔn)電動機容量由額定功率表達(dá)， 電機額定功率應(yīng)略高于工作要求功率。電動機容量由運行時發(fā)熱條件限定。功率為：
式中： ———電動機輸出功率；
———工作所需輸入功率；
———總效率；
功率由工作阻力和運動參數(shù)得，
或
式中： ———阻力；N
———工作機的線速度；m/s
———工作機的阻力矩；N*m
———工作機的轉(zhuǎn)速；r/min
———工作機的效率；
由于電動機的輸出功率已經(jīng)知道，而且傳動效率也在97.5%以上，所以可得工作所需輸入功率：
==12.7
3.3 選擇電動機的型號
Y系列電動機，通常選用同步轉(zhuǎn)速為1500r/min和1000r/min；綜合尺寸、價格以及傳動比的特點及大小，我選用1500r/min的電動機比較方便。查閱實用機械手冊選用Y2—61—4型號電動機，額定功率13Kw，轉(zhuǎn)速1500r/min，滿載時功率因數(shù)為0.88。
4 帶傳動的設(shè)計
4.1 確定計算功率Pc
Pc=KaP Ka—工況系數(shù)，取Ka=1.1
P---電動機輸出功率
則Pc=KaP=1.1×13=14.3KW
4.2 確定V帶型號
根據(jù)計算功率Pc=14.3KW，電動機轉(zhuǎn)速n1=1500r/min，選用A型帶。
4.3 確定傳動比i
i=＝
n1—小帶輪轉(zhuǎn)速（r/min） n2—大帶輪轉(zhuǎn)速（r/min）
Dp1—小帶輪直徑（mm） Dp1—小帶輪直徑（mm）
根據(jù)使用要求，滾筒轉(zhuǎn)速n2為1050r/min，電動機轉(zhuǎn)速n1為1500r/min，
則 i==1.43
4.4 計算帶輪直徑
小帶輪的基準(zhǔn)直徑d1＞75mm。初步設(shè)定小帶輪基準(zhǔn)直徑為140mm，則大帶輪基準(zhǔn)直徑d2=id1=1.43×140=200，結(jié)合本設(shè)計要求，取d2為300mm。
4.5 驗算帶速V
V===11（m/s）
V＜25m/s，帶速滿足要求。
確定中心距a和帶的基準(zhǔn)長度Ld0
由式0.7×（d1+d2）≤a0≤2×（d1+d2），初定中心距a0=（1.5～2）d2=
1.8×300=540mm，即308≤a0≤880，取a0=550mm。
由傳動的幾何關(guān)系可計算帶的基準(zhǔn)初值Ld0
Ld0=2×a0+×（d1+d2）+=2×550+×（140+300）+=1802.44mm
查手冊，取Ld=1800mm，因此帶傳動的實際中心距為：
a≈a0+=550+≈550mm
實際中心距的調(diào)節(jié)范圍應(yīng)控制在a-0.015Ld≤a≤a+0.03Ld之間，安裝時應(yīng)保證
的最小中心距為523mm，最大中心距為604mm。
4.6 校核小帶輪的包角α1
P1=180o-×57.3o=180o-×57.3o=163.33o＞120o，合格。
4.7 計算所需V帶根數(shù)Z
查詢機械設(shè)計手冊，得單根V帶的基本額定功率P0=2.74KW，額定功率增量△P0=0.28KW，小帶輪包角修正系數(shù)Kp=0.96，長度修正系數(shù)Kl=1.01，
則Z===2.03，取Z=2，選用2根V帶。
5 螺旋輸送器（攪龍）的設(shè)計
5.1 攪龍的結(jié)構(gòu)型式
攪龍有整片式，環(huán)帶式和槳葉式三種，整片式螺旋輸送器適用于未脫粒的谷物等，故選擇整片式，其結(jié)構(gòu)簡單，如圖所示：
5.2攪龍葉片的螺旋角
螺旋角越大，生產(chǎn)率越高，但工作費力；螺旋角越小，生產(chǎn)率越低，但工作省力。若角過大，甚至不能工作，為此要尋求出合理的螺旋角。
Vf==Rω
Vz= Vf×cos（+ ）= Rω cos（+ ）
Vf---輸送物的絕對速度 Vn---法向速度，Vn=Rcos Vz---沿Z軸的分速度 ---當(dāng)摩擦角存在時，輸送物質(zhì)點的運動方向與法線的夾角 ---Vn與Z軸的夾角
攪龍工作時，我們希望輸送物有較大的軸向輸送速度，以提高輸送能力。對Vz求導(dǎo)數(shù)，即可求得值。
因為
Vz= Rω cos（+ ）
對Vz求導(dǎo)數(shù)，并令=0，可得=-。
水稻對鐵皮的摩擦角=17o30′。
螺旋角=-時，具有最大的軸向速度。所以螺旋葉片平均半徑處的螺旋角=23o30′。
5.3 攪龍的內(nèi)徑D1
對谷粒輸送攪龍，攪龍內(nèi)徑就是軸的直徑，工作時往往因為其細(xì)而長容易出現(xiàn)剛度不夠而變形，故設(shè)計時常按必須保證攪龍軸有一定的剛度來考慮其內(nèi)徑的大小，常按下面經(jīng)驗式選取。
即
D1=（0.02～0.03）L L---攪龍的長度
由于本設(shè)計需要，取L=1258mm，則D1=25mm。
5.4攪龍的外徑D2
1.1.1 對谷粒輸送攪龍，常取D2=120～200毫米，視機的大小而異，小機的取小些。本設(shè)計取D2=124mm。
5.5攪龍的螺距S
攪龍的螺距常按如下經(jīng)驗式選?。?
=0.7～1
螺距大，生產(chǎn)率高，工作費力，所以不管是谷粒攪龍或割臺攪龍，設(shè)計時的比值通常都選用接近下限值。故取S=87mm。
5.6攪龍的轉(zhuǎn)速n
在保證生產(chǎn)率條件下，轉(zhuǎn)速越低越好，因轉(zhuǎn)速越高，消耗功率越多，而且易造成谷粒的破碎和飛散。
對普通谷粒輸送攪龍，取n=100～300r/min。由實際情況，取n=200r/min。
5.7計算攪龍帶輪直徑
小帶輪的基準(zhǔn)直徑Ф1＞75mm。初步設(shè)定聯(lián)動皮帶輪的基準(zhǔn)直徑Ф1=90mm
因為 i=== 故Ф2=412，取標(biāo)準(zhǔn)值Ф2=400mm。
取其標(biāo)準(zhǔn)值190mm。
6 機架的設(shè)計
6.1 斷面形狀和尺寸選擇
機架的抗拉和抗壓剛度，一般僅與其斷面面積的大小有關(guān)，與斷面的形狀無關(guān)。但在承受彎曲力矩與扭轉(zhuǎn)力矩時，則機架的抗彎剛度與抗扭剛度不僅與其截面面積的大小有關(guān)，且與斷面形狀有很大的關(guān)系，即與其斷面慣性矩成正比。
1） 圓形空心截面的抗彎剛度及抗扭剛度都比較好。
2）長方形空心斷面對提高長邊方向的抗彎剛度十分顯著，但抗扭強度較差。
3）外形尺寸大而壁薄的斷面比外形尺寸小而壁厚的斷面的抗彎剛度和抗扭剛度都高，空心結(jié)構(gòu)的剛度比實心結(jié)構(gòu)的剛度大。
4）工字形斷面在高度方向上抗彎剛度最大，但抗扭剛度較差。
5）不封閉的斷面的抗扭剛度極差。
因此，基于該脫粒機的抗扭剛度不必太高，而要有足夠的抗壓抗彎剛度，機架斷面選擇矩形空心截面，可采用角鋼。
6.2 結(jié)構(gòu)設(shè)計
當(dāng)脫粒機工作時，機架過高，不易投入物料；機架過低，容易疲勞。一般，H=550～950mm最好。
結(jié)合滾筒和電動機的安裝，電動機可以通過螺釘固定在平板上，脫粒機的機架設(shè)計為如下結(jié)構(gòu)。其結(jié)構(gòu)由熱軋不等邊角鋼焊接而成，具有足夠的強度和剛度支撐整個機器的正常工作。
結(jié)構(gòu)如下圖所示：
此外，機架底座可以改裝成帶輪子的，那樣移動就更加方便。
7 主軸的設(shè)計和校核
7.1 選擇軸的材料
軸的材料主要是碳素鋼和合金鋼，根據(jù)傳動的功率和一些參數(shù)選擇材料，最常用材料為45#鋼；查《機械設(shè)計手冊》表11.1查得毛坯直徑200毫米，硬度217—255HBS，抗拉強度極限=640Mpa，屈服強度極限=355 Mpa，彎曲疲勞極限=275 Mpa，剪切疲勞極限=155 Mpa。
7.2 確定軸的直徑
軸是機械傳動的中的重要零件，設(shè)計時應(yīng)滿足合理的結(jié)構(gòu)，足夠的強度等，軸設(shè)計根據(jù)軸上零件的定位和固定要求，以及加工和裝配要求，合理定出軸的結(jié)構(gòu)外形和全部尺寸過程。
設(shè)計軸時必需要先對軸的直徑進(jìn)行必要的估算，由于本實用型的單株脫粒機的主軸主要承受扭矩作用，所以只需按軸所受的轉(zhuǎn)矩來進(jìn)行計算。
扭矩強度條件為:
= = []
式中： ———軸的扭轉(zhuǎn)切應(yīng)力，Mpa
T———扭矩，N.mm
n———軸的轉(zhuǎn)速，r/min
P———軸傳遞功率，Kw
[]———許用扭轉(zhuǎn)切應(yīng)力，Mpa
—軸的抗扭截面模量，
對實心圓軸，=/16，可得軸的直徑：
=
式中C取決于許用扭轉(zhuǎn)切應(yīng)力[]的系數(shù)，當(dāng)彎矩相對轉(zhuǎn)矩較小時，C取較小值，[]取大值，反之，C取較大值，[]取較小值。
幾種軸材料的[]和C值：
軸的材料
1
35
45
40 213
[]
12—20
12—25
20—30
30—40
40—52
C
160—135
148—125
135—118
118—107
107—98
根據(jù)軸材料為45#鋼，對軸的直徑進(jìn)行估算=44.5毫米，因此所設(shè)計的主軸直徑取為48毫米。
7.3 軸的結(jié)構(gòu)設(shè)計
主要承受扭矩的零件，從強度方面考慮，則以圓截面最好，空心矩形的次之，脫粒室的寬度為1000毫米，軸承的寬度為45毫米，輪子邊緣到腔壁的間距為140毫米和114毫米，其中主動輪的一端為140毫米，另一端為114毫米。則軸長：L=1000+140+114+2 45=1344毫米。其結(jié)構(gòu)如圖所示。
7.4 軸上零件的周向定位
皮帶輪的周向定位采用平鍵連接，由手冊查得平鍵截面b×h=14×9；根據(jù)輪轂的寬度選用平鍵為14×9×70；同時軸肩高度h一般取為：h=（0.07—0.1）d，取為0.08×48=3.84毫米。
7.5 滾筒主軸的強度校核
1. 對軸進(jìn)行受力分析并簡化軸的受力
將滾筒上的受力簡化為集中力通過鍵作用于軸上，軸承對軸的支點反力，軸受到的作用力有:軸承的支點反力、滾筒的作用力、電機的轉(zhuǎn)矩，如下圖所示：
2. 計算水平面上的剪切力和彎矩，畫出水平彎矩圖，并找出危險截面。
剪切力:
==992N =1984N
F點彎矩：
=550mm=5.456Nmm
剪切圖及彎矩圖如下：
3. 計算垂直面上的剪切力和彎矩，畫出水平彎矩圖，并找出危險截面。
剪切力：
==1732N =3464N
F點彎矩：
=550mm=9.526Nmm
剪切圖及彎矩圖如下：
4. 計算轉(zhuǎn)矩
N
對滾筒主軸的強度用第四強度理論校核，則有：
校核結(jié)果：
46.38
所以受到最大力的截面安全；軸的強度安全，滿足使用要求。
7.6 鍵連接的強度校核
鍵連接是把軸和軸上的零件聯(lián)接起來的形式，鍵連接具有結(jié)構(gòu)簡單，工作可靠等優(yōu)點。
根據(jù)軸徑d，查鍵的標(biāo)準(zhǔn)，得到鍵的截面尺寸b×h=14×9；根據(jù)輪轂的寬度，查鍵的標(biāo)準(zhǔn)，取L=70；
校核條件：擠壓強度
剪切應(yīng)力
式中： T———傳遞的扭矩；N.mm
d———軸徑；mm
h———鍵的高度；mm
l———鍵的工作長度，對A型鍵l=L-b；mm
———許用擠壓應(yīng)力；M
———鍵的許用剪應(yīng)力；M
鍵的材料：45號鋼
校核： = =14.18M
= =4.558M
故滿足擠壓強度和剪切強度。
8 軸承的選用
軸承的作用是支撐軸及軸上零件，因滾動軸承已標(biāo)準(zhǔn)化，所以我們只需要選型就可以了。
滾動軸承應(yīng)根據(jù)載荷性質(zhì)、大小、方向等要求來選擇。本設(shè)計軸只承受徑向載荷且承載能力不要求很高，所以我們選擇深溝球軸承6000系列。
查袖珍機械設(shè)計師手冊第二版。
選軸承尺寸如下：d=40，D=62，B2=12，rmin=0.6，極限轉(zhuǎn)速12000（油潤滑），軸承代號61908
9 總結(jié)
單株脫粒機具有的生產(chǎn)率高，能耗低，各項性能指標(biāo)優(yōu)越的特點，目前已運用于我國農(nóng)業(yè)領(lǐng)域。通過此設(shè)計，讓我對專業(yè)知識的運用，有了一定程度上的把握。將理論和實踐結(jié)合起來，進(jìn)一步鞏固、加深和擴展了所學(xué)知識。
通過此次課題設(shè)計，學(xué)習(xí)和掌握了常見機械零件，機械傳動裝置或簡單機械的一般設(shè)計方法和原則。培養(yǎng)了分析和解決機械設(shè)計問題的能力，為以后進(jìn)行相關(guān)的設(shè)計工作打下了基礎(chǔ)。
使我在使用掌握計算機繪圖、運用并熟悉相關(guān)設(shè)計資料（包括手冊，標(biāo)準(zhǔn)和規(guī)范等）以及進(jìn)行經(jīng)驗估算等方面有了一定程度的提高。
總之，此設(shè)計為我學(xué)習(xí)和工作打下了堅實的基礎(chǔ)，為今后從事設(shè)計工作奠定了廣闊，深厚的基礎(chǔ) 。
由于水平有限，在設(shè)計中如有不正之處，請指導(dǎo)老師不吝指正。
參考文獻(xiàn)
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致 謝
20
Fault diagnostic systems for agricultural machinery
Geert Craessaerts, Josse De Baerdemaeker, Wouter Saeys
Fault detection and diagnosis in process industry have attracted a lot of attention recently. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial and statistical methods. From a modelling perspective, the methods can rely on quantitative, semi-quantitative and qualitative models. At the other end of the spectrum, there are historical data-based methods that do not make use of any form of model information but rely only on historical process data. The basic aim of this study is to emphasize the importance of introducing more advanced multivariate fault diagnostic systems on agricultural machinery. Up till now, farmers and contractors still observe the process in order to detect process and sensor failures which can disturb the actions of the controllers and cause severe damage to the machine. In the future, the complete reliance on human operators for the correct functioning of these systems will become too risky, due to the increasing complexity of this type of machinery. A systematic and comparative study of various fault diagnostic methods, from an agricultural machinery perspective, is provided in this study. The different fault diagnostic techniques, investigated in scientific literature, are compared and evaluated on a common set of criteria. Typical requirements of a fault diagnostic system for agricultural machinery are adaptability to process changes, user-friendliness, quick detection and robustness. Based on these findings, a hybrid framework of qualitative model-based fault detection techniques and pattern recognition-based methods, which rely on historical process data, is proposed as the most suitable fault diagnostic technique.
As a first step towards more advanced fault detection and isolation systems, the general applicability of intelligent neural network techniques like supervised self-organizing maps (SOMs) and back-propagation neural networks is illustrated for the detection and isolation of sensor failures on a New Holland CX combine harvester. Pattern recognition techniques, such as neural networks, were found to be very suitable for this kind of application because a lot of historical process data is available since the recent generation of combine harvesters is equipped with a wide range of sensors and actuators, which are continuously monitored. Moreover, these pattern recognition techniques allow quick detection, are easy to use and are able to adapt their structure and/or model parameters based on new measurement data. Since there is room for improvement of these standard techniques, suggestions for future research concerning fault diagnosis on agricultural machinery are given as well.
1. Introduction
The introduction of process control has made a remarkable contribution to the world of agricultural technology. In the past, different processes on agricultural machinery were performed by human operators, but now the larger part is handled in an automatic manner by low and high-level control actions (Coen, Saeys, Missotten, & De Baerdemaeker, 2007; Coen, Vanrenterghem, Saeys, & De Baerdemaeker,2008; Craessaerts, Saeys, Missotten, & De Baerdemaeker, in press). At a supervisory level, human operators still observe the process in order to detect process malfunctions, abnormal events and/or sensor failures which can disturb the actions of the controllers and cause severe damage to the whole process. However, this supervisory task becomes increasingly difficult for agricultural machinery operators due to the ever increasing workload and machine complexity they have to deal with. As a result, human operators often make erroneous decisions concerning the supervisory control of these machines which can have a significant economic, environmental and/or safety impact. Operating on uncertain or missing data may cause improper control actions and consequently the system will not be operating optimally. One of the next challenges for control engineers involved with the automation of agricultural machinery will be the automation of fault detection and diagnosis to further lighten the job of the operator.
In this context, a fault can be defined as a departure from an acceptable range of an observed variable or a calculated parameter associated with a process (Himmelblau, 1978). This defines a fault as a process abnormality or symptom, such as too high a pressure or too high a temperature of a hydrostatic pump. Faults can have different sources and can be classified into three classes of failures: caused by malfunctioning sensors and/or actuators, structural changes in the process or a sudden change of model parameters. The latter one is mainly caused by external disturbances whose dynamics are not taken into account in the process model. In this paper, an overview will be given of the different diagnostic techniques described in the literature for fault detection and diagnosis. Up till now, most of these techniques have been applied in the process industry because of the critical safety norms these processes deal with. It will be shown that fault diagnostic systems have not been given much attention yet in agricultural machinery research. However, these techniques could be of high value at a supervisory control level for agricultural machinery. Based on a formulation of the specific characteristics that a fault diagnostic system for agricultural machinery should include, a suggestion will be made of the most suitable diagnostic methods. Finally, the usefulness of artificial neural networks as a fault diagnostic tool for sensor failure detection will be investigated for an example case. This case study encompasses the detection and isolation of sensor failures on a New Holland CX combine harvester by means of self-organizing maps (SOMs) and back-propagation neural networks.
2. Fault detection and isolation techniques
In the literature, fault diagnosis methods are broadly classified into three categories based on the type and amount of prior knowledge they use. A distinction can be made between quantitative model-based methods, qualitative model-based methods and process history-based methods (Venkatasubramanian, Rengaswamy, Yin, and Kavuri, 2003). The basic a priori knowledge that is needed for fault diagnosis is the set of possible failures and the relationship between the observations (symptoms) and the failures. This a priori domain knowledge may be derived from:
- a fundamental understanding of the process using first principles models: such knowledge is referred to as causal or model-based knowledge,
- historical process data: in this case, the knowledge is referred to as process history-based knowledge. The model-based a priori knowledge can be broadly classified as qualitative or quantitative. The model is usually developed based on some fundamental understanding of the physics of the process. In quantitative models this understanding is expressed in terms of a mathematical functional relationship between the inputs and outputs of the system. In contrast, in qualitative model equations these relationships are expressed in terms of heuristic functions cantered around different units in a process.
An excellent review of the different fault detection and isolation (FDI) techniques discussed in scientific literature is given by Venkatasubramanian, Rengaswamy, and Kavuri (2003); Venkatasubramanian, Rengaswamy, Kavuri, and Yin (2003); Venkatasubramanian, Rengaswamy, Yin, et al. (2003). In this section, these different techniques will be briefly communicated in order to highlight the advantages and shortcomings of the discussed techniques. This critical evaluation will be based on a formulation of the desirable characteristics the ideal FDI system should possess. The conclusions drawn from this review will be of high importance for readers wishing to implement a FDI system for their particular application.
2.1. Desired characteristics of a fault diagnostic system
In Venkatasubramanian, Rengaswamy, Yin, et al. (2003), an overview is given of the characteristics the ideal FDI should possess:
- A quick detection and diagnosis of faults: a trade-off should be made between quick detection of faults and sensitivity to measurement noise. A high sensitivity to noise will lead to frequent false alarms during normal operation.
- Isolation of faults: the fault diagnostic system should be able to make a distinction between different types of failures.
- Robustness: the fault diagnostic system should be robust with respect to measurement noise and model uncertainties.
- Novelty identification: the fault diagnostic system should be able to recognize the occurrence of novel faults and not misclassify these as one of the known malfunctions or as normal operation.
- Classification error estimate: in order to make the system more reliable for the user, a prior estimate of the classification errors that can occur should be provided.
- Adaptability: most processes in the real world are time varying because of changes in environmental conditions and/or product characteristics. The diagnostic system should be adaptable to these changes.
- Explanation facility: besides the ability of the system to identify the source of malfunctioning, the diagnostic system should also provide an explanation of how the fault originated and propagated into the current situation.
- Low modeling requirements: the modeling effort for the development of the diagnostic classifier should be as low as possible.
- Low computational requirements: with an eye on an implementation of the diagnostic classifier on a system with fast dynamics, the implementation algorithm should be of low complexity.
- Multiple fault identification: the fault diagnosis system should be able to identify multiple faults occurring at the same time.
2.2. Quantitative model-based methods
In quantitative model-based FDI methods, one makes use of the inconsistencies, also called the residuals, between the actual and predicted process behavior. As a first step, the residuals between the real system response and the modeled system response are calculated. Any inconsistency, expressed as residuals, can be used for detection and isolation purposes. The residuals should be close to zero when no fault occurs, but show ‘significant’ values when the underlying system changes. In a final step, a decision algorithm will make the appropriate fault diagnosis.
As mentioned above, the generation of the diagnostic residuals requires an explicit mathematical model of the system. Consequently, the complexity and reliability of the resulting FDI system depends on the kind of modeling method and comparison strategy that was used (Venkatasubramanian, Rengaswamy, Yin, et al., 2003). Either first-principles models, black-box or statistical models can be used.
First-principles models are based on a physical understanding of the process and are of high complexity when dealing with supervisory control and diagnosis of a whole plant which very often has non-linear characteristics. As a result, first-principle models are seldom used for fault diagnosis. Most of the FDI methods use discrete black-box and/or statistical plant models such as input–output or state space models and assume linearity of the plant (Venkatasubramanian, Rengaswamy, Yin, et al., 2003).
Process faults usually cause a change in the state variables, a change in the model parameters and/or a change in the output of the process. Based on the process model, one can estimate the non-measurable state variables or model parameters by the observed outputs and inputs using state estimation and parameter estimation methods. Typical state estimation techniques used in fault diagnosis are the Kalman filter and the Luemberger observer (Clark, 1978; Frank, 1986; Patton, Chen, & Nielsen, 1995). These reconstruct the unknown states based on the measurements or subsets of the measurement data. The Luemberger observer is typically used in a deterministic setting while the Kalman filter is mainly used for stochastic processes (Betta and Pietrosanto, 2000). As a consequence, the deviations (residuals) of the model parameters and/or state variables from the normal situation can be used as a fault indicator. Similarly, parity relations (Gertler, 1995; Willsky, 1976) check the consistency of the modeled process output with the real measured process output. Any observed inconsistency would result in a high output residual and indicate the occurrence of a typical fault. Once the residuals are calculated, they have to be evaluated. When designing the decision algorithm, a trade-off should be made between fast and reliable fault detection. In most applications of residual observation, a simple threshold function is used. However, more scientific statistical and/or neural network classifiers are preferred (Koppen-Seliger, Frank, & Wolff, 1995).
When evaluating quantitative model-based fault detection systems, it should be noted that these techniques require a high modeling effort and are generally restricted to linear systems and some specific non-linear systems. For a general non-linear system, linear approximations can be poor and hence the effectiveness of the method can be greatly reduced.
However, thanks to the method of disturbance decoupling, the robustness can be maximized by minimizing the effect of unknown disturbances, like measurement and process noise, and unmodelled process behavior. In this approach, all uncertainties are treated as disturbances and filters are designed to decouple the effects of faults and uncertainties such that these can be differentiated (Frank & Wunnenberg, 1989; Viswanadham & Srichander, 1987).
2.3. Qualitative model-based methods
As noted above, when the a priori domain knowledge is developed from a fundamental understanding of the process by means of physical process knowledge, it is called causal model-based knowledge. When the physics of the process is expressed as mathematical functional relations between inputs, outputs and states of the system a quantitative modeling approach is used as mentioned in the previous section. When the physical relationships are expressed by means of qualitative, non-quantified functions the term qualitative modeling is used. A distinction should be made between the causal models and the abstraction hierarchies (Venkatasubramanian, Rengaswamy, & Kavuri, 2003).
In a first attempt, knowledge-based expert systems, which mimic the fault detection by human experts, were investigated as a tool for fault diagnosis. However, the rule base, which consists of ‘if–then’ rules, grows rapidly with increasing complexity of the system. Another problem of this approach is the lack of insight into the physics of the system which means that it will fail when new conditions are encountered that were not defined in the rule base (Venkatasubramanian, Rengaswamy, & Kavuri, 2003). The need for a reasoning tool which can model the system in a qualitative way and describe it by a causal structure which is not as rigid as a numerical or analytical model has led to the development of different qualitative modeling methods, like digraphs and fault tree structures (Venkatasubramanian, Rengaswamy, & Kavuri, 2003).
A digraph is a graph with directed arcs between the nodes which represents the cause–effect relation of a system. The directed arcs lead from the ‘cause’ nodes to the ‘effect’ nodes. As a result, it is an efficient way of representing the observed symptoms or patterns of a fault in a graphical way. Maurya, Rengeswany, and Venkatasubramanian (2007) proposed a digraph-based fault detection framework to select a possible candidate set of faults based on the incipient response of the process.
Fault trees are mainly used in analyzing the system reliability and safety. The tree has different layers with nodes and at each node logic operations like AND and OR are performed for propagation. Fault trees serve to represent the propagation path of a fault from their origin to their top level of occurrence.
Another way of presenting model-based knowledge is through the development of abstraction hierarchies. These are based on the decomposition of the process system into different subsystems. The main idea is to gain insight in the overall process behavior by inspection of the laws governing the different subsystems. The failure of a higher-level subsystem will be caused by the failure of one or more of the subsystems. The main source of malfunctioning can then be found by making use of a bottom-up description, which describes what various units with certain functions are used for and how these serve the higher-level systems.
When evaluating qualitative model-based fault detection systems, it can be concluded that these techniques are of high value when an abundance of process experience is available which is not numerically detailed. One of the main advantages of qualitative methods based on deep-knowledge is that they provide an explanation of the path of propagation. However, their complexity will increase very rapidly with the complexity of the system and, in comparison with quantitative model-based techniques; they suffer from the resolution problem because no detailed interval or order of magnitude information is available.
2.4. Process history-based methods
In contrast to the model-based fault diagnosis approaches where a process model is needed a priori, only a large amount of historical process data is needed in process history-based fault diagnosis methods. Different kinds of features are then extracted from these historical process data. The extracted features can be of qualitative and/or quantitative nature (Venkatasubramanian, Rengaswamy, Kavuri, et al., 2003). In the former case a distinction can be made between expert systems and trend modeling methods.
An expert system typically consists of a set of heuristic rules derived from a knowledge base. Since considerable process knowledge is often available from experienced engineers and/or operators of the process plant, this can be incorporated. A fuzzy rule base serves as the ideal framework for the incorporation of human knowledge into a fault diagnosis system. Several authors have discussed expert system applications for fault diagnosis of specific systems (Chester, Lamb, & Dhurjati, 1984; Henley, 1984; Rich, Venkatasubramanian, Nasrallah, & Matteo, 1989).
In the case of qualitative trend analysis, the different process signals are monitored and the qualitative analysis of their trends provides valuable information for the identification of underlying abnormalities in the process. These trends can be extracted from a qualitative analysis of the shape of the dynamics of a sensor signal. Venkatasubramanian, Rengaswamy, Kavuri, et al. (2003) state that a suitable classification and analysis of process trends can detect the fault earlier and lead to a quick repair of the faulty sensor.
When extracting quantitative features from a historical data set, the fault diagnosis problem can be solved by pattern recognition techniques. The main goal of pattern recognition is to classify the quantitative features into different predetermined classes based on the interrelationship of these features. The number of classes equals n + 1, with n the number of faults to be isolated. An extra class is needed to cluster the data points which correspond to the normal mode of operation. These pattern recognition techniques can be broadly classified into statistical and non-statistical (neural network) ones.
Traditionally used neural network classifiers are the supervised back-propagation algorithm, self-organizing maps and support vector machines. Some of them will be investigated further in detail in Section 4.2.
When evaluating process his