機(jī)械設(shè)計(jì)制造及其自動(dòng)化-pe10自行車無級(jí)變速器設(shè)計(jì)-資料下載頁(yè)
2024-12-06 03:52本頁(yè)面
【導(dǎo)讀】行部分改裝，從而形成了自行車的無級(jí)變速裝置。該裝置通過八個(gè)鋼球利用摩擦力將動(dòng)力進(jìn)行輸入。輸出，用一對(duì)斜齒輪進(jìn)行分度調(diào)速，從而使自行車在～。級(jí)變速器被用于自行車方面可以大大改善自行車的使用性能，方便廣大消費(fèi)者使用。無級(jí)變速器分為機(jī)械無級(jí)變速器，液壓傳動(dòng)無級(jí)變速器，電力傳動(dòng)無級(jí)變速器三種，展，但當(dāng)時(shí)由于受材質(zhì)與工藝方面的條件限制，進(jìn)展緩慢。因此在這種形式下，機(jī)械無級(jí)變速器獲得迅速和廣泛的發(fā)展。主要研制和生產(chǎn)的。式以及切削機(jī)床的Kopp型無級(jí)變速器等，但品種規(guī)格不多，產(chǎn)量不大，年產(chǎn)量?jī)H數(shù)千臺(tái)。與此同時(shí)，無級(jí)變速器專業(yè)協(xié)會(huì)、行業(yè)協(xié)會(huì)。機(jī)械無級(jí)變速器是一種傳動(dòng)裝置，其功能特征主要是：在輸入轉(zhuǎn)速不變的情況下，可以看出采用無級(jí)變速器，尤其是配合減速傳動(dòng)時(shí)進(jìn)一步擴(kuò)大其變速范。故無級(jí)變速器目前已成為一種基本的通用傳動(dòng)形式，應(yīng)用于紡織、輕工、食品、兩腿放在前輪二側(cè)。杠桿式曲柄無級(jí)傳動(dòng)裝置固定在前輪的前上方，通過左右曲柄桿上
【正文】 rt has been submitted to RollsRoyce 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 RollsRoyce and Aachen university of Technology), with no prior domain knowledge, nor information of system failure . Techniques Employed OBS used a number of analysis techniques given the limited timescales: IVisualisation, 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 . IISignature 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. IIITemplate Analysis This method allows instantaneous sampleby –sample novelty detection, suitable for online implementation. Using a plementary approach to Signature Analysis, this method also models normal system behavior. Results confirmed the observation made using previous methods. IVNeural work Predictor Similarly useful for online analysis, this method uses an automated predictor of system behaviour(a neural work predictor), in which previously identified events were confirmed, and further significant episodes were detected. 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 31 actual occurrences within the life of the system, to be performed by Aachen University of Technology. 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, pared 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 “fleetwide” 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. 32 2 Introduction Oxford BioSignals Limited This document reports on the initial analysis conducted by Oxford BioSignals of manufacturing process challenge data provided by RollsRoyce, in conjunction with Aachen University of Technology(AUT). Oxford BioSignals Limited(OBS) is a worldclass 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 online and offline implementations at multiple levels: as a research tool, a test bed system, a ground support tool, an onboard monitoring system, an offline 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 prehensive 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. 33 3 External References Acpanying 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 MultiLayer 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. 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 feedrate. The following data streams were recorded during each test, each sampled at a rate of 20 KHz: Ax – accelerat
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