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XX學(xué)院本科畢業(yè)設(shè)計（論文）開題報告
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畢業(yè)設(shè)計（論文）題目
無級變速自行車設(shè)計
開題報告（闡述課題的目的、意義、研究現(xiàn)狀、研究內(nèi)容、研究方案、進度安排、預(yù)期結(jié)果、參考文獻等）
畢業(yè)設(shè)計（論文）的目的和意義：
改進現(xiàn)有齒輪式有級變速自行車不足。
1、換擋問題。一般變速自行車為前后輪雙齒輪組，以滿足更多種變速比。這種方式需要車主熟悉掌握變速的檔位數(shù)以搭配獲得最佳效果。無級變速自行車能夠?qū)崿F(xiàn)傳動比的連續(xù)改變，免去記憶復(fù)雜的前后輪檔位搭配，增加了易用性。
2、重心問題。普通變速自行車的變速齒輪集中在一側(cè)，造成重心偏移。易倒。無級變速采用后輪2個并排，中間放置變速器，重心居中，便于轉(zhuǎn)彎操控。
3、鏈條問題。傳統(tǒng)變速自行車存在鏈條在齒輪組空隙中調(diào)整時易掉鏈的問題。無級變速自行車采取金屬帶式無級變速方式，錐形金屬帶夾在兩個錐形輪之間，不易掉鏈。而且金屬帶以及錐形輪盤較普通齒輪鏈條性能更加優(yōu)異。減小了保養(yǎng)難度。
4、維修問題。普通變速自行車因構(gòu)建過多，增加了維修難度，而無級變速自行車零件簡單，維修難度低，
畢業(yè)設(shè)計（論文）的研究現(xiàn)狀：
自行車發(fā)展到現(xiàn)在已經(jīng)有傳統(tǒng)的自行車演變成無級變速自行車，現(xiàn)代的無級變速自行車可謂是形式多樣，五花八門，以下是當(dāng)今社會上存在的部分無級變速自行車。
1、低座無級變速自行車
由低矮形車架把一個作驅(qū)動的前輪和一個作導(dǎo)向的后輪連接在一塊的自行車，帶靠背的座椅安裝在車架中部，騎行者可斜躺著坐在座椅上，兩腿放在前輪二側(cè)。杠桿式曲柄無級傳動裝置固定在前輪的前上方，通過左右曲柄桿上的滑塊鉸接鏈條交替?zhèn)鲃忧拜?。操縱把手裝于前輪的正上方，由鋼絲繩牽引后輪轉(zhuǎn)向。這樣就不會干擾車子的方向操縱。
2、人力腳踏式無級變速自行車
自行車的行走和變速不用成組鏈輪和鏈條傳動，成本低、重量輕，可實現(xiàn)無級變速，速度轉(zhuǎn)換快，速比大。
3、帶傳動無級變速自行車
該無級變速自行車的結(jié)構(gòu)簡單、易于加工，可以實現(xiàn)大規(guī)模成批生產(chǎn)。
4、前置往復(fù)式無級變速自行車
5、純滾動式四個檔位無級變速自行車
6、無鏈無級變速自行車
7、蓄能型-全自動無級變速自行車
8、便攜式高安全型無級變速自行車
畢業(yè)設(shè)計（論文）主要內(nèi)容和要求：
本次設(shè)計的手推割草機適于家庭庭院及私人花園等小型草地使用，不需電力或柴油為動力，無廢氣及噪音等污染，符合環(huán)保要求。人們在漫步中割草，既鍛煉身體又可實現(xiàn)修整草坪的目的。手推滾刀式割草機的工作原理，當(dāng)推動扶手架使割草機在草地上前行時，行走輪的內(nèi)齒輪帶動小齒輪旋轉(zhuǎn)，使得滾刀也一起轉(zhuǎn)動，通過滾刀刃與定刀刃形成的剪切動作，將草坪上多余高度的草割斷。
畢業(yè)設(shè)計（論文）應(yīng)完成的主要工作：
1.調(diào)研相關(guān)產(chǎn)品，搜集相關(guān)資料。
2.分析資料，擬定可行的設(shè)計方案。
3.對比方案，確定最終方案。
4.詳細設(shè)計設(shè)備結(jié)構(gòu)。
5.繪制裝配圖及部分零件圖。
6.撰寫設(shè)計說明書。
畢業(yè)設(shè)計（論文）的研究方案：
鋼球長錐式無級變速器結(jié)構(gòu)很簡單，且使用參數(shù)更符合我們此次設(shè)計的要求，但由于在調(diào)速過程中，怎樣使鋼環(huán)移動有很大的難度，需要精密的裝置，如果此裝置用于自行車，成本會大大的提高，顯得不合理。
而鋼球外錐式無級變速器的結(jié)構(gòu)也比較簡單，原理清晰，各項參數(shù)也比較符合設(shè)計要求，故選擇此變速器。只是字選用此變速器的同時須對該裝置進行部分更改。
須更改的部分是蝸輪蝸桿調(diào)速裝置部分。因為我們是選用了8個鋼球，曲線槽設(shè)計，一個曲線槽跨度是900，也就是說自行車從最大傳動比調(diào)到最小傳動比，需要使其轉(zhuǎn)過900，而普通蝸輪蝸桿傳動比是1/8，那么其結(jié)構(gòu)和尺寸將完全不符合我們設(shè)計的要求。為此，我們想到了將它們改為兩斜齒輪傳動，以用來調(diào)速。選用斜齒輪是因為斜齒輪傳動比較平穩(wěn)。在設(shè)計過程中，將主動斜齒輪的直徑設(shè)計成從動斜齒輪的3/4，這樣只要主動輪轉(zhuǎn)動1200，那么從動輪就會轉(zhuǎn)動900，符合設(shè)計要求。
畢業(yè)設(shè)計（論文）進度安排：
畢業(yè)設(shè)計（論文）主要參考資料：
[1]濮良貴,紀名剛.機械設(shè)計[M].第八版.西安:高等教育出版社, 2005.
[2]孫恒,陳作模.機械原理[M].第六版.西安:高等教育出版社, 2000.
[3]徐灝.機械設(shè)計手冊[M].第三卷.北京:機械工業(yè)出版社, 1991.
[4]吳宗澤,羅圣國.機械設(shè)計課程設(shè)計手冊[M].第三版.北京:高等教育出版社, 2006.
[5]周良德,朱泗芳.現(xiàn)代工程圖學(xué)[M].湘潭:湖南科學(xué)技術(shù)出版社, 2000.
[6]周有強.機械無級變速器[M].成都:機械工業(yè)出版社, 2001.
[7]李新,洪泉,王艷梅.國內(nèi)外通用標準件手冊[M].南京:江蘇科技出版,鳳凰出版?zhèn)髅郊瘓F, 2006.
[8]葛志淇.機械零件設(shè)計手冊[M].天津:冶金工業(yè)出版社,1980.
指導(dǎo)教師意見：
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無級變速自行車設(shè)計
1 前言
自行車的誕生與發(fā)展已有幾百年了，在自行車的發(fā)展歷程中自行車的結(jié)構(gòu)有過幾次重大的變化，這些變化使自行車的設(shè)計發(fā)展中出現(xiàn)過“山窮水復(fù)疑無路，柳暗花明又一村”。每一次重大的變化都是自行車的設(shè)計思想上的一個大的突破，每一次大的變化都使自行車的發(fā)展進入了一個新時代。
1791年，法國人西弗拉克發(fā)明了最原始的自行車。它只有兩個輪子而沒有傳動裝置，人騎在上面，需用兩腳蹬地驅(qū)車向前滾動。近些年來，我國機械工業(yè)有了很大的發(fā)展，這給今后機床夾具的發(fā)展提出了更高的要求。
2 機械無級變速器的發(fā)展概況
無級變速器分為機械無級變速器，液壓傳動無級變速器，電力傳動無級變速器三種，但本設(shè)計任務(wù)要求把無級變速器安裝在自行車上，所以一般只能用機械無級變速器，所以以下重點介紹機械無級變速器。
機械無級變速器最初是在19世紀90年代出現(xiàn)的，至20世紀30年代以后才開始發(fā)展，但當(dāng)時由于受材質(zhì)與工藝方面的條件限制，進展緩慢。直到20世紀50年代，尤其是70年代以后，一方面隨著先進的冶煉和熱處理技術(shù)，精密加工和數(shù)控機床以及牽引傳動理論與油品的出現(xiàn)和發(fā)展，解決了研制和生產(chǎn)無級變速器的限制因素；另一方面，隨著生產(chǎn)工藝流程實現(xiàn)機械化、自動化以及機械要改進工作性能，都需要大量采用無級變速器。因此在這種形式下，機械無級變速器獲得迅速和廣泛的發(fā)展。主要研制和生產(chǎn)的國家有美國、日本、德國、意大利和俄國等。產(chǎn)品有摩擦式、鏈式、帶式和脈動式四大類約三十多種結(jié)構(gòu)形式。
國內(nèi)無級變速器是在20世紀60年代前后起步的，當(dāng)時主要是作為專業(yè)機械配套零部件，由于專業(yè)機械廠進行仿制和生產(chǎn)，例如用于紡織機械的齒鏈式，化工機械的多盤式以及切削機床的Kopp型無級變速器等，但品種規(guī)格不多，產(chǎn)量不大，年產(chǎn)量僅數(shù)千臺。直到80年代中期以后，隨著國外先進設(shè)備的大量引進，工業(yè)生產(chǎn)現(xiàn)代化及自動流水線的迅速發(fā)展，對各種類型機械無級變速器的需求大幅度增加，專業(yè)廠才開始建立并進行規(guī)?；a(chǎn)，一些高等院校也開展了該領(lǐng)域的研究工作。經(jīng)過十幾年的發(fā)展，國外現(xiàn)有的幾種主要類型結(jié)構(gòu)的無級變速器，在國內(nèi)皆有相應(yīng)的專業(yè)生產(chǎn)廠及系列產(chǎn)品，年產(chǎn)量約10萬臺左右，初步滿足了生產(chǎn)發(fā)展的需要。與此同時，無級變速器專業(yè)協(xié)會、行業(yè)協(xié)會及情報網(wǎng)等組織相繼建立。定期出版網(wǎng)訊及召開學(xué)術(shù)信息會議進行交流。
3 無級變速自行車研究現(xiàn)狀
自行車發(fā)展到現(xiàn)在已經(jīng)有傳統(tǒng)的自行車演變成無級變速自行車，現(xiàn)代的無級變速自行車可謂是形式多樣，五花八門，以下是當(dāng)今社會上存在的部分無級變速自行車。
1、低座無級變速自行車
由低矮形車架把一個作驅(qū)動的前輪和一個作導(dǎo)向的后輪連接在一塊的自行車，帶靠背的座椅安裝在車架中部，騎行者可斜躺著坐在座椅上，兩腿放在前輪二側(cè)。杠桿式曲柄無級傳動裝置固定在前輪的前上方，通過左右曲柄桿上的滑塊鉸接鏈條交替?zhèn)鲃忧拜?。操縱把手裝于前輪的正上方，由鋼絲繩牽引后輪轉(zhuǎn)向。這樣就不會干擾車子的方向操縱。
2、人力腳踏式無級變速自行車
一種人力腳踏式無級變速自行車，在自行車車架兩側(cè)面的中軸上，安裝有錐面相對的變速輪盤組成的主動輪，主動輪兩側(cè)安裝有腳蹬兩變速輪盤輪沿掛有三角皮帶，兩盤面間安裝有壓縮彈簧；在車架的前斜梁上，安裝有由變速桿操縱可前后移動的挺桿，挺桿的近變速輪盤端安裝有可使兩變速輪盤靠近或分離的插件；在自行車后軸上的后輪輪輻兩側(cè)面支承有附輪；車架后斜梁上在三角皮帶上方安裝有可推壓三角皮帶張緊的張緊輪。自行車的行走和變速不用成組鏈輪和鏈條傳動，成本低、重量輕，可實現(xiàn)無級變速，速度轉(zhuǎn)換快，速比大。
3、帶傳動無級變速自行車
一種無級變速自行車，改進了現(xiàn)有自行車的動力傳動機構(gòu)。該自行車的動力傳動機構(gòu)包括以下部件：小動輪、小定輪等構(gòu)成。其特征在于自行車的動力傳動機構(gòu)包括以下部件：小動輪、小定輪等，與自行車后軸上的飛輪軸套固定連接，小動輪在撥叉控制下沿軸滑動；大動輪、大定輪、大動輪撥叉，大動輪、大定輪也呈錐形，兩輪大小形狀一致，錐面相對，組成帶有Ｖ形溝槽的大傳動輪，固定在自行車中軸上，大動輪在撥叉控制下沿軸滑動；Ｖ型傳動帶鑲在大小輪的溝槽中；Ｖ型帶張緊裝置裝在后軸上，其支承輪支撐傳動帶；調(diào)速器裝在車把附近，與閘線連接，閘線帶動調(diào)節(jié)大小動輪位置的撥叉。 這種無級變速自行車通過帶傳動來實現(xiàn)自行車的無級變速，傳動平穩(wěn)、噪音低、調(diào)速操作方便、變速范圍大；同時該無級變速自行車的結(jié)構(gòu)簡單、易于加工，可以實現(xiàn)大規(guī)模成批生產(chǎn)。
4、前置往復(fù)式無級變速自行車
針對自行車的驅(qū)動、乘座和避震進行改進。包括：乘騎者坐靠休閑式椅，兩腳蹬踏前置的兩個懸搖桿曲柄，可進行弧形的曲線往復(fù)運動，用腳掌面的蹬踏角度或用手直接調(diào)動搖桿上力臂的長短實現(xiàn)無級變速，高效能的帶動撓性件驅(qū)動后輪；還包括裝卸方便且不互換的休閑式座椅和防落物防盜的可帶走座椅；簡化的全避震使乘坐舒適并使貨架攜帶的物品減小了顛簸
5、純滾動式四個檔位無級變速自行車
一種純滾動式四個檔位無級變速自行車，其中在中軸上的中心齒輪嚙合連接有一級行星輪和二級行星輪，中心齒輪的兩側(cè)分別套裝有推動盤，一側(cè)固定在腳蹬輪軸上，另一側(cè)固定在鏈輪上；二級行星輪和中心齒輪為棘輪總成與鏈輪嚙合連接，在中軸和后軸的車架體上固定有座盤，座盤上固定有升降檔位彈簧。隨時變增減速檔位，對自行車零部件無影響，制造簡單，性能可靠，操作簡單，使用方便。
6、無鏈無級變速自行車
一種無鏈條傳動，可隨意變換車速的自行車。該自行車包括車輪、把手、三角架和踏拐等，橫梁左端設(shè)有后齒輪、大齒輪和正反齒輪，橫梁右端設(shè)有中軸齒輪，齒輪與拐軸齒輪嚙合，偏心連桿的上端和杠桿的右端同軸裝在定位槽板的滑槽中，杠桿的左端與齒條連接，齒條與正反齒輪嚙合，橫梁上方設(shè)有拉簧、活動支架和鋼絲拉索。該自行車結(jié)構(gòu)簡單，調(diào)速方便靈活，經(jīng)久耐用，適合各種型號。
7、蓄能型-全自動無級變速自行車
一種蓄能型一全自動無級變速自行車，屬于交通工具技術(shù)領(lǐng)域。本實用新型的目的通過如下技術(shù)方案實現(xiàn)：主要由設(shè)置每側(cè)腳蹬上的長型齒盤交替工作，通過同側(cè)的鏈條傳動同側(cè)的飛輪，飛輪連同帶動設(shè)置在輪骨內(nèi)的發(fā)條內(nèi)端發(fā)條外端同輪骨固定。騎行時由于每側(cè)長型齒盤的作用，通過鏈條對同側(cè)的發(fā)條交替蓄能，從而實現(xiàn)全自動無級變速。本實用新型是現(xiàn)代變速自行車的換代產(chǎn)品。
8、便攜式高安全型無級變速自行車
一種新式樣的自行車。其特征是由行走機構(gòu)，車椅式直立車龍頭轉(zhuǎn)向機構(gòu)，杠桿式無級變速驅(qū)動機構(gòu)。本裝置是由足踏杠桿式無級變速機構(gòu)，車架可橫向折疊，驅(qū)動大車輪在前面，導(dǎo)向小車輪在后邊的行走機構(gòu)與帶靠背車坐椅式的直立車龍頭轉(zhuǎn)向機構(gòu)組成的自行車裝置。該裝置形體式樣，較為奇特但騎行舒適，更安全，并能折疊便攜帶。
4 無級變速自行車的研究意義及實用價值
改進現(xiàn)有齒輪式有級變速自行車不足。
1、換擋問題。一般變速自行車為前后輪雙齒輪組，以滿足更多種變速比。這種方式需要車主熟悉掌握變速的檔位數(shù)以搭配獲得最佳效果。無級變速自行車能夠?qū)崿F(xiàn)傳動比的連續(xù)改變，免去記憶復(fù)雜的前后輪檔位搭配，增加了易用性。
2、重心問題。普通變速自行車的變速齒輪集中在一側(cè)，造成重心偏移。易倒。無級變速采用后輪2個并排，中間放置變速器，重心居中，便于轉(zhuǎn)彎操控。
3、鏈條問題。傳統(tǒng)變速自行車存在鏈條在齒輪組空隙中調(diào)整時易掉鏈的問題。無級變速自行車采取金屬帶式無級變速方式，錐形金屬帶夾在兩個錐形輪之間，不易掉鏈。而且金屬帶以及錐形輪盤較普通齒輪鏈條性能更加優(yōu)異。減小了保養(yǎng)難度。
4、維修問題。普通變速自行車因構(gòu)建過多，增加了維修難度，而無級變速自行車零件簡單，維修難度低，
5、舒適度問題。現(xiàn)有變速自行車最多擁有前7后9共63種檔位，但其跳躍性檔位設(shè)置有可能仍然使某些人找不到適于自己的傳動比。無級變速自行車可以滿足各種人的不同需要。
6、轉(zhuǎn)彎問題。采取前輪小后輪大的設(shè)計方式，增加了轉(zhuǎn)彎的靈活性。由于主要構(gòu)件集中于后輪，重心靠后，可以減小前輪小容易前翻的問題。
7、上坡問題。普通變速自行車由于前后輪同時放置變速器，重心較無級變速自行車靠前。上坡難度比后置無級變速自行車大。
8、使用價值。可以有效降低成本，裝配難度，維修難度，生產(chǎn)難度都有所降低，適于普通家居生活。
9、操作難度降低，適用于中國最早一代使用自行車卻正在慢慢老去的群體。只需轉(zhuǎn)動旋鈕即可實現(xiàn)變速。
10、外觀新穎時尚
3 結(jié)束語
綜上，機械工業(yè)是國民經(jīng)濟的支柱產(chǎn)業(yè)，現(xiàn)代機械制造技術(shù)是機械工業(yè)賴以生存和發(fā)展的重要保證。自行車隨著科技的發(fā)展使計算機技術(shù)、數(shù)控技術(shù)、控制論及系統(tǒng)工程與制造技術(shù)結(jié)合為制造系統(tǒng)，形成現(xiàn)代制造工程學(xué)。在機械制造中，可能會使用很多先進的鑄造技術(shù)，這些制造技術(shù)可以提高勞動生產(chǎn)率，提高加工精度，減少廢品，可以擴大工藝范圍，改善操作者的勞動條件。因此，機械制造的一項重要工藝設(shè)備，這也給今后無極變速技術(shù)的發(fā)展提出了更高的要求。
參考文獻
[1]濮良貴,紀名剛、機械設(shè)計[M]、第八版、西安:高等教育出版社, 2005、
[2]孫恒,陳作模、機械原理[M]、第六版、西安:高等教育出版社, 2000、
[3]徐灝、機械設(shè)計手冊[M]、第三卷、北京:機械工業(yè)出版社, 1991、
[4]吳宗澤,羅圣國、機械設(shè)計課程設(shè)計手冊[M]、第三版、北京:高等教育出版社, 2006、
[5]周良德,朱泗芳、現(xiàn)代工程圖學(xué)[M]、湘潭:湖南科學(xué)技術(shù)出版社, 2000、
[6]周有強、機械無級變速器[M]、成都:機械工業(yè)出版社, 2001、
[7]李新,洪泉,王艷梅、國內(nèi)外通用標準件手冊[M]、南京:江蘇科技出版,鳳凰出版?zhèn)髅郊瘓F, 2006、
[8]葛志淇、機械零件設(shè)計手冊[M]、天津:冶金工業(yè)出版社,1980
5
附錄1 翻譯原文及譯文
Doc No: P0193-GP-01-1
Doc Name: Analysis of Manufacturing
Process Data Using
QUICK TechnologyTM
Issue： 1
Data： 20 April ，2006
Name(Print)
Signature
Author：
D.Clifton
Reviewer：
S.Turner
22
Table of Contents
1 Executive Summary 4
1.1 Introdution 4
1.2 Techniques Employed 4
1.3 Summary of Results 4
1.4 Observations 4
2 Introdution 6
2.1 Oxford BioSignals Limited 6
3 External References 7
4 Glossary 7
5 Data Description 8
5.1 Data types 8
5.2 Prior Experiment Knowledge 8
5.3 Test Description 8
6 Pre-processing 10
6.1 Removal of Start/Stop Transients 10
6.2 Removal of Power Supply Signal 10
6.3 Frequency Transformation 10
7 Analysis I-Visualisation 13
7.1 Visualisation of High-Dimensional Data 13
7.2 Visualising 5-D Manufacturing Process Data 13
7.3 Automatic Novelty Detection 15
7.4 Conclusion of Analysis I-Visualisation 16
8 Analysis II-Signature Analysis 17
8.1 Constructing Signatures 17
8.2 Visualising Signatures 19
8.3 Conclusion of Analysis II-Signature Analysis 23
9 Analysis III-Template Analysis 24
9.1 Constructing a Template of Normality 24
9.2 Results of Novelty Detection Using Template Analysis 25
9.3 Conclusion of Analysis III-Template Analysis 26
10 Analysis IV-None-linear Prediction 27
10.1 Neural Networks for On-Line Prediction 27
10.2 Results of Novelty Detection using Non-linear Prediction 27
10.3 Conclusion of Analysis IV-Non-linear Prediction 28
11 Overall Conclusion 29
11.1 Methodology 29
11.2 Summary of Tesults 29
11.3 Future Work 29
12 Appendix A-NeuroScale Visualisations 31
Table of Figures
Figure 1- Test 90. From top to bottom: Ax, Ay, Az, AE, SP against time t(s)
Figure 2- Power spectra for Test 19 after removal of 50Hz power supply contribution. The top plot shows a 3-D “l(fā)andspace” plot of each spectrum. The bottom plot shows a “contour” plot of the same information, with increasing signal power shown as increasing colour from black to red
Figure 3- Power spectra for Test 19 after removal of all spectral components beneath power threshold
Figure 4- Az against time (in seconds) for Test 19,before removal of low-power frequency components
Figure 5- Az against time (in seconds) for Test 19, after removal of low-power frequency components
Figure 6- SP for an example test, showing three automatically-detecrmined states:S1-drilling in (shown in green); S2-drill-bit break-through and removal (shown in red); S3-retraction (shown in blue)
Figure 7- Example signature of variable plotted against operating-point
Figure 8- Power spectra for test 51, frequency (Hz) on the x-axis between [0 fs/2]
Figure 9- Average significant frequency
Figure 10- Visualisation of AE signatures for all tests
Figure 11- Visualisation of Ax broadband signatures for all tests
Figure 12- Visualisation of Ax average-frequency signatures for all tests
Figure 13- Novelty detection using a template signature
Figure 14-
1 Executive Summary
1.1 Introduction
The purpose of this investigation conducted by Oxford BioSignals was to examine and determine the suitability of its techniques in analyzing data from an example manufacturing process. This report has been submitted to Rolls-Royce for the expressed of assessing Oxford BioSignals’ techniques with respect to monitoring the example process.
The analysis conducted by Oxford BioSignals (OBS) was limited to a fixed timescale, a fixed set of challenge data for a single process (as provided by Rolls-Royce and Aachen university of Technology), with no prior domain knowledge, nor information of system failure .
1.2 Techniques Employed
OBS used a number of analysis techniques given the limited timescales:
I-Visualisation, and Cluster Analysis
This powerful method allowed the evolution of the system state (fusing all available data types) to be visualised throughout the series of tests. This showed several distinct modes of operation during the series, highlighting major events observed within the data, later correlated with actual changes to the system’s operation by domain experts.
Cluster analysis automatically detects which of these events may be considered to be “abnormal”, with respect to previously observed system behavior .
II-Signature represents each test as a single point on a plot, allowing changes between tests to be easily identified. Abnormal tests are shown as outlying points, with normal tests forming a cluster.
Modeling the normal behavior of several features selected from the provided data, this method showed that advance warning of system failure could be automatically detected using these features, as well as highlighting significant events within the life of the system.
III-Template Analysis
This method allows instantaneous sample-by –sample novelty detection, suitable for on-line implementation.
Using a complementary approach to Signature Analysis, this method also models normal system behavior. Results confirmed the observation made using previous methods.
IV-Neural network Predictor
Similarly useful for on-line analysis, this method uses an automated predictor of system behaviour(a neural network predictor), in which previously identified events were confirmed, and further significant episodes were detected.
1.3 Summary of Results
Early warning of system failure was independently identified by the various analysis methods employed.
Several significant events during the life of the process were correlated with actual known events later revealed by system experts.
Changes in sensor configurations are identified, and periods of system stability (in which tests are similar to one another) are highlighted.
This report shall be used as the basis for further correlation of detected events against actual occurrences within the life of the system, to be performed by Aachen University of Technology.
1.4 Observations
Based on this limited study, OBS are confident that their techniques are applicable to condition monitoring of the example manufacturing process as follows:
Evidence shows that automated detection of system novelty is possible, compared to its “normal” operation.
Early warning of system distress may be provided, giving adequate time to take preventative maintenance actions such that system failure may be avoided.
Provision “fleet-wide” analysis is possible using the techniques considered within this investigation.
The involvement of domain knowledge from system experts alongside OBS engineers will be crucial in developing future implementations. While this “blind” analysis showed that OBS modelling techniques are appropriate for process monitoring, it is the coupling of domain knowledge with OBS modelling techniques that may provide optimal diagnostic and prognostic analysis.
2 Introduction
2.1 Oxford BioSignals Limited
This document reports on the initial analysis conducted by Oxford BioSignals of manufacturing process challenge data provided by Rolls-Royce, in conjunction with Aachen University of Technology(AUT).
Oxford BioSignals Limited(OBS) is a world-class provider of Acquisition, Data Fusion, Neural Networks and other Advanced Signal Processing techniques and solutions branded under the collective name QUICK Technology. This technology not only provides for health and quality assurance monitoring of the operational performance of equipment and plant.
QUICK Technology has been extensively proven in the field of gas turbine monitoring with both on-line and off-line implementations at multiple levels: as a research tool, a test bed system, a ground support tool, an on-board monitoring system, an off-line analysis tool and a “fleet” manager.
Many of the techniques employed by OBS may be described as novelty detection methods. This approach has a significant advantage over many traditional classification techniques in that it is not necessary to provide fault data to the system during development. Instead, providing a sufficiently comprehensive model of the condition can be identified automatically. As information is discovered regarding the causes of these deviations it is then possible to move from novelty detection to diagnosis, but the ability to identify previously unseen abnormalities is retained at all stages.
3 External References
Accompanying documentation providing further information on the data sets is available in unnumbered documents.
4 Glossary
AUT- Aachen University of Technology
GMM- Gaussian Mixture Model
MLP- Multi-Layer Perception
OBS- Oxford BioSignals Ltd.
5 Data Description
The following sections give a brief overview of the data set obtained by visual inspection of the data.
4.1 Data types
The data provided were recorded over a number of tests. Each test consisted of a similar procedure, in which an automated drill unit moved towards a static metallic disk at a fixed velocity (“feed”), a hole was drilled in the disk at that same feed-rate.
The following data streams were recorded during each test, each sampled at a rate of 20 KHz:
Ax – acceleration of the disk-mounting unit in the x-plane1 ,
Ay- acceleration of the disk-mounting unit in the y-plane1 ,
Az- acceleration of the disk-mounting unit in the z-plane1 ,
AE-RMS acoustic emission, 50-400 KHz2,
SP-power delivered to the drill spindle3.
Tests considered in this investigation used three drill-prices (of identical product specification) as shown in Table 1.
Table 1-Experiment Parameters by Test
Drill Number
Test Numbers
Drill Rotation Rate
Feed Rate
1
[12]
1700RPM
80 mm/min
2
[3127]
1700RPM
80 mm/min
3
[130194]
1700RPM
120mm/min
Note that tests 16,54,128,129 were not provided, thus a series of 190 tests are analysed in this investigation. These 190 tests are labeled as shown in Table 2.
Table 2 –Test indices used in this report against actual test numbers
Test Indices
Actual Test Number
[115]
[115]
[1652]
[1753]
[53125]
[55127]
[126190]
[130194]
4.2 Prior Experiment Knowledge
4.2.1 Normal Tests
AUT indicated that tests [10110] could be considered “normal processes”.
4.2.2 AE Sensor Placement
AUT noted that the position of the acoustic emission sensor was altered prior to test 77, and was adjusted prior to subsequent tests. From inspection of AE data, it appears that AE measurements are consistent after test 84, and so:
·AE is assumed to be unusable for tests [176] –the sensor records only white noise;
·AE is assumed to be usable, but possibly abnormal, for tests [7783] –the sensor position is being adjusted, resulting in extreme variation in measurements;
·AE is assumed to be usable for tests [94190] –the sensor position is held constant during these tests.
Thus, the range of tests assumed to be normal [10110] should be reduced to [84110] when AE is considered.
4.3 Test Description
Data recorded for during a typical test are shown in Figure 1. The duration of this test is approximately t=51 seconds. This section uses this test to illustrate a typical process, as described by AUT.
Drill power-on and power-off events may be seen at the start and end of the test as transient spikes in SP.
The drill unit is then moved towards the static disk at the constant feed rata specified in Table 1, between t=12 and 27 seconds. This corresponds to approximately constant values of SP during that period, approximately zero AE, and very lowamplitude acceleration in x-,y-,and z- planes.
At t=27 seconds, the drill makes contact with the static disk and begins to drill into the metal. This corresponds to a step-change in SP to a higher lever, staying approximately constant until t=38 seconds. During this time, AE increases significantly to a largely constant but non-zero value. The values Ax and Az increase throughout this drilling operation, while the value of Ay remains approximately zero (as it does throughout the test).
At t=38 seconds, the tip of the drill-bit passes through the rear face of the disk. The value of SP increases until t=44 seconds. During this period, AE reaches correspondingly high values, while Ax and Az decrease in amplitude.
At t=44 seconds, the direction of the drill unit is reversed, and the drill is retracted from the metal disk. Until t=46 seconds, the value of SP and AE decrease rapidly. A transient is observed in Ax and Az at t =44 seconds, with vibration amplitude decreasing until t=46 seconds.
At t=46 seconds, the drill-bit has been completely retracted from the metal disk, and the unit continues to be withdrawn at the feed rate until the end of the test. The value of SP decreases during this period(noting the power-off transient at the very end of the test), while the values of all three acceleration channels and AE are approximately zero.
6 .Pre-processing
4.4 Removal of Start/Stop Transients
Assuming that normal and abnormal system behaviour will be evident from data acquired during the drilling process, prior to analysis, each test was shortened by retaining only data between the start and stop events, shown as transients in SP. For example, for the test shown in Figure 1, this corresponds to retaining the period [1350] seconds.
4.5 Removal of Power Supply Signal
The 50 Hz power supply appears with in each channel, and was removed prior to analysis by application of a band-stop filter with stop-band [4951] Hz.
4.6 Frequency Transformation
Data for each test were divided into windows of 4096 points. A 4096-point FFT for was performed using data within each window, for Ax,Ay and Az channels. This corresponds to approximately 5 FFTs per second of data，similar to the QUICK system used in aerospace analysis， shown to provide sufficient resolution for identifying frequency-based events indicative of system abnormality.
For the analyses performed in this investigation, all spectral components of Ax, Ay, and Ay occurring at frequency f with power Pf below some threshold Pf
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