無級變速自行車設(shè)計-電動車變速器設(shè)計【16張CAD高清圖紙及說明書】【YC系列】

【溫馨提示】=【1】設(shè)計包含CAD圖紙 和 DOC文檔，均可以在線預(yù)覽，所見即所得，dwg后綴的文件為CAD圖，超高清，可編輯，無任何水印，充值下載得到【資源目錄】里展示的所有文件=【2】若題目上備注三維，則表示文件里包含三維源文件，由于三維組成零件數(shù)量較多，為保證預(yù)覽的簡潔性，店家將三維文件夾進(jìn)行了打包。三維預(yù)覽圖，均為店主電腦打開軟件進(jìn)行截圖的，保證能夠打開，下載后解壓即可。=【3】特價促銷，拼團(tuán)購買，均有不同程度的打折優(yōu)惠，詳情可咨詢QQ：1304139763 或者 414951605=【4】 題目最后的備注【YC系列】為店主整理分類的代號，與課題內(nèi)容無關(guān)，請忽視 附錄1 翻譯原文及譯文Doc No: P0193-GP-01-1Doc Name: Analysis of ManufacturingProcess Data Using QUICK TechnologyTMIssue：1Data：20 April ，2006Name(Print)SignatureAuthor：D.CliftonReviewer：S.Turner22Table of Contents1Executive Summary41.1Introdution41.2Techniques Employed41.3Summary of Results41.4Observations42Introdution62.1Oxford BioSignals Limited63External References74Glossary75Data Description85.1Data types85.2Prior Experiment Knowledge85.3Test Description86Pre-processing106.1Removal of Start/Stop Transients106.2Removal of Power Supply Signal106.3Frequency Transformation107Analysis I-Visualisation137.1Visualisation of High-Dimensional Data137.2Visualising 5-D Manufacturing Process Data137.3Automatic Novelty Detection157.4Conclusion of Analysis I-Visualisation168Analysis II-Signature Analysis178.1Constructing Signatures178.2Visualising Signatures198.3Conclusion of Analysis II-Signature Analysis239Analysis III-Template Analysis249.1Constructing a Template of Normality249.2Results of Novelty Detection Using Template Analysis259.3Conclusion of Analysis III-Template Analysis2610Analysis IV-None-linear Prediction2710.1Neural Networks for On-Line Prediction2710.2Results of Novelty Detection using Non-linear Prediction2710.3Conclusion of Analysis IV-Non-linear Prediction2811Overall Conclusion2911.1Methodology2911.2Summary of Tesults2911.3Future Work2912Appendix A-NeuroScale Visualisations31Table 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 thresholdFigure 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 componentsFigure 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-pointFigure 8- Power spectra for test 51, frequency (Hz) on the x-axis between 0 fs/2Figure 9- Average significant frequency Figure 10- Visualisation of AE signatures for all testsFigure 11- Visualisation of Ax broadband signatures for all testsFigure 12- Visualisation of Ax average-frequency signatures for all testsFigure 13- Novelty detection using a template signatureFigure 14-1 Executive Summary1.1 IntroductionThe 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 EmployedOBS 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 systems 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 ResultsEarly 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 ObservationsBased 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 Introduction2.1 Oxford BioSignals LimitedThis 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 ReferencesAccompanying documentation providing further information on the data sets is available in unnumbered documents.4 GlossaryAUT- Aachen University of Technology GMM- Gaussian Mixture Model MLP- Multi-Layer PerceptionOBS- Oxford BioSignals Ltd.5 Data DescriptionThe following sections give a brief overview of the data set obtained by visual inspection of the data. 4.1 Data typesThe 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 TestDrill NumberTest NumbersDrill Rotation RateFeed Rate1121700RPM80 mm/min231271700RPM80 mm/min31301941700RPM120mm/minNote 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 numbersTest IndicesActual Test Number1151151652175353125551271261901301944.2 Prior Experiment Knowledge4.2.1 Normal TestsAUT indicated that tests 10110 could be considered “normal processes”.4.2.2 AE Sensor PlacementAUT 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 DescriptionData 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-processing4.4 Removal of Start/Stop TransientsAssuming 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 SignalThe 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 TransformationData 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 Pfh were discarded. Time-domain signals were reconstructed by performing an inverse FFT operation on each spectral window of 4096 points.Figure 2 shows the spectral power content of Az for Test 19 after removal of the 50 Hz power supply signal, from 021 seconds, with each FFT shown between 0 fs/2 Hz. Frequency content throughout this test is typical for all tests: the majority of significant spectral peaks are concentrated during the drilling operation(between 14 and 21 seconds, in this test). As a hole is drilled in the metal disk, power is concentrated at higher and higher frequencies, usually reaching a highest frequency(here, approximately 5.8 KHz with a spike at 7.4 KHz) when the drill-bit passes through the disk.Figuer 3 shows the same test are removal of all components with power Pf0.1. This retains the significant peaks in the power spectral, whilst removing components assumed to be insignificant due to their low power. Figure 4 and Figure 5 show the corresponding time-series data for Ax in test 19. After removal of low-power frequency components, the time-series retains only the episodes in which significant-power vibrations were observed, which are used as the basis for detection of system abnormality by several of the analysis methods used within this investigation.Figure 2-Power spectra for Test 19 after removal of 50 Hz power supply contribution. The top plot shows a 3-D “l(fā)andscape” 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 spectral 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 componentsFigure 5-Az against time(in seconds) for Test 19, after removal of low-power frequency components.Analysis I-VisualisationThis section describes the first of four analysis techniques applied to the manufacturing process data-set.4.7 Visualisation of High-Dimensional Data4.7.1 Constructing a 2-D VisualizationThe use of large numbers of measured variables introduces problems in the visualization of the resulting data. A collection of temperatures, pressures, etc. forms a high-dimensional representation of the state of a system, but this is not readily interpreted by an operator. Neuroscale allows the visualization of systems that have high-dimensionality by mapping data to lower numbers of dimensions(typically two,for visual inspection). It attempts to preserve the inter-pattern distances in the high-dimensional data. Data which are close together in high-dimensional space are typically kept close together in 2-D space, and data that are originally far apart remain well separated after projection.The projection is performed using a non-linear function from the datas k dimensional space down to 2-D for visualization purposes. In this investigation, k is 5:Ax, Ay, Az, AE, SP are the high-dimensional sample vectors. The creation of a non-linear mapping from 5-D space to 2-D requires sample data from across the range of tests. In order to reduce the large number of available sample data to a quantity suitable for constructing the mapping, a summary of the data-set is required. Each test was summarized by a number of prototype 5-D vectors using the k-means clustering algorithm(in which a large number of data are represented by a smaller number of prototype vectors). The non-linear mapping was trained using the prototype 5-D vectors from all tests.4.7.2 Automatic Test SegmentationTo allow the examination of the 5-D data using visualization, it is convenient to divide the drilling process in to three stages, corresponding to the typical behaviour of the process described in Section 5.3.A heuristic algorithm was produced to perform automatic segmentation into three episodes using the SP channel, as illustrated in Figure 6(which shows a low-pass filtered version of SP superimposed on the original signal as a red line). The three states identified correspond to :State S1: the approximately constant-power (or slightly decreasing) initial period of drilling;State S2: the peak-power period where the drill-bit passes through the disk and is removedState S3: the approximately constant-power period of retraction.Note that this segmentation is only the identification of the times of onset and offset of each of the three described states, for the purposes of graphical display as described in the next sub-section.公司機(jī)密牛津信號分析機(jī)構(gòu)文件號：P0193-GP-01=1文件名：制造分析 進(jìn)程數(shù)據(jù)使用 快速標(biāo)記技術(shù)論點：1日期：2006.4.20 姓名簽名作者D.Clifton審核S.Turner目錄1 執(zhí)行概要（文章綜述）引言引用的技術(shù)結(jié)論摘要觀察資料、報告2 引言牛津信號分析機(jī)構(gòu)3 引用國外的參考文獻(xiàn)4 術(shù)語表5 數(shù)據(jù)描述數(shù)據(jù)類型試驗狀況簡介測試描述6 預(yù)處理移除開始、終止瞬態(tài)數(shù)據(jù)移除電源干擾信號頻率變換7 分析處理1-可視化高維數(shù)據(jù)分析5維機(jī)械加工數(shù)據(jù)自動信號檢測分析方案1-可視化的結(jié)論8 分析處理2-信號處理分析構(gòu)建信號系統(tǒng)波形分析信號分析結(jié)論9 分析處理3-基于模板分析的數(shù)據(jù)分析構(gòu)建普通信號模板使用模板分析捕獲信號的結(jié)論分析結(jié)論10 分析處理5-非線性預(yù)測分析 基于在線預(yù)測的神經(jīng)網(wǎng)絡(luò) 基于非線性預(yù)測的神經(jīng)網(wǎng)絡(luò)得到的結(jié)論 非線性預(yù)測結(jié)論11 系統(tǒng)結(jié)論方法學(xué)結(jié)論概述前景工作12 附錄：神經(jīng)網(wǎng)絡(luò)分析關(guān)于圖表的列表圖表1-測試90,從上到下分別是:AX,AY,AZ,AE,SP相對于時間的坐標(biāo)圖表2-在移除50HZ電源干擾信號后測試19的能量光譜圖上邊區(qū)域顯示的是每一個光譜的三維空間.底部區(qū)域顯示的是相同的信息,都是表示隨著由黑色到紅色顏色的增加信號的能量也在增加.圖表3-在移除所有低于最低能量的光譜成分后,所得的測試19的能量譜圖表5-在移除低電源頻率成分后繪制的AZ-時間(以秒為單位)圖圖表6-一個典型的測試顯示三個自動定義的步驟:第一步-鉆進(jìn)(綠色顯示);第二步-鉆頭鉆削與移動(紅色顯示);第三步-反應(yīng)(藍(lán)色顯示)圖表7-加工區(qū)域的y方向典型信號的多樣性圖表8-能量譜,X軸上的頻率在0 fs/2圖表9-平均頻率圖表10-所有測試AE信號的可視化圖表11-所有測試X方向?qū)拵盘柕目梢暬瘓D表12-所有測試X方向平均頻率信號的可視化圖表13-用模板信號去進(jìn)行信號鑒定1 執(zhí)行概要1.1 引言由牛津信號分析機(jī)構(gòu)組織進(jìn)行的這次調(diào)查的目的是檢驗和判定其技術(shù)在分析從典型機(jī)械加工中得到的數(shù)據(jù)時的適用性能。這則關(guān)于牛津信號分析機(jī)構(gòu)在關(guān)于掌控典型加工中的技術(shù)的評估報告已經(jīng)被呈交給了Rous-Royce.由牛津信號分析機(jī)構(gòu)組織進(jìn)行的分析僅限于固定的時間標(biāo)度。一個簡單加工整套令人質(zhì)疑的數(shù)據(jù)（就像由Rous-Royce和亞深工業(yè)大學(xué)提供的一樣），沒有相關(guān)的經(jīng)驗知識，也沒有系統(tǒng)崩潰的征兆。1.2 引用的技術(shù)OBS引用了一系列只在有限的時間間隔里給定的分析技術(shù)。1-可視化,以及聚類分析這種權(quán)威的方法允許通過一系列的測試構(gòu)建出系統(tǒng)狀態(tài)（即提煉所有現(xiàn)有數(shù)據(jù)類型）的演變。在這些測試過程中，這種方法在這些資料中能夠觀察到的主要方面提供了幾個截然不同的運(yùn)作模式。然后相關(guān)的系統(tǒng)運(yùn)作的實際改變也將由一些權(quán)威專家提出。聚類分析能夠綜合考慮系統(tǒng)的特性習(xí)慣，自動從這些系統(tǒng)事件中識別出異常事件。2-信號再現(xiàn)每一次實驗將一個個單獨的點的形成描述在圖表中，允許實驗點之間存在一定的誤差，超出誤差范圍的實驗點就可以很