【正文】 續(xù)到 t=38秒。 這一節(jié)用這個(gè)測(cè)試去說明一個(gè)典型的加工，就像亞琛工業(yè)大學(xué)所說的。 AX圓板加工在 X方向的加速度 AY圓板加工在 Y方向的加速度 AZ圓板加工在 Z方向的加速度 AERMS原聲，以 50400HZ播放 SP給鉆軸提供的電源 這個(gè)調(diào)查中的測(cè)試是用三種型號(hào)的鉆同（由相同的產(chǎn)品性能），如下表 1所示 表 1測(cè)試的試驗(yàn)參數(shù) 鉆頭編號(hào) 測(cè)試編號(hào) 鉆頭轉(zhuǎn)速 進(jìn)給量 1 [12] 1700RPM 80 mm/min 2 [3127] 1700RPM 80 mm/min 3 [130194] 1700RPM 120mm/min 注意到測(cè)試 16， 54， 128， 129沒有被介紹，因此只分析了 190次測(cè)試，這 190次測(cè)試列于下表 2 表 2在這則報(bào)告中針對(duì)每次測(cè)試所使用的代號(hào) 測(cè)試代號(hào) 實(shí)際測(cè)試編號(hào) [115] [115] [1652] [1753] [53125] [55127] [126190] [130194] 試驗(yàn)狀況簡(jiǎn)介 正常試驗(yàn) 亞琛工業(yè)大學(xué)表示試驗(yàn) 10到 110被 稱為正常加工。 3 引用國(guó)外的參考文獻(xiàn) 在未編號(hào)的文獻(xiàn)中可以獲得提供更深一步信號(hào)的關(guān)于數(shù)據(jù)庫(kù)的必備資料。牛津信號(hào)分 析機(jī)構(gòu)是一家世界級(jí)的信號(hào)分析供應(yīng)者，數(shù)據(jù)融合、神經(jīng)網(wǎng)絡(luò)和其他先進(jìn)信號(hào)加工技術(shù)以及許多技術(shù)在其“快速”技術(shù)名下，這項(xiàng)技術(shù)不僅提供壽命和質(zhì)量保證，而且還提供機(jī)器設(shè)備運(yùn)轉(zhuǎn)過程的掌控方法。 觀察資料，報(bào)告 基于以上有限的研究， OBC有信息使得它的技術(shù)運(yùn)用于以下典型加工的條件控制過程中： 與正常運(yùn)作比較 ,事實(shí)說明系統(tǒng)信號(hào)的自動(dòng)檢測(cè)是由可能的； 它將提供系統(tǒng)故障的早 期報(bào)警，這將使得有足夠的時(shí)間采取措施以避免加工失敗的產(chǎn)生。在加工過程中的一些重要的事件與后來由系統(tǒng)專家證實(shí)的事件是息息相關(guān)的。它是用一種完整的方法去進(jìn)行信號(hào)分析。 2信號(hào)再現(xiàn) 每一次實(shí)驗(yàn)將一個(gè)個(gè)單獨(dú)的點(diǎn)的形成描述在圖表中，允許實(shí)驗(yàn)點(diǎn)之間存在一定的誤差，超出誤差范圍的實(shí)驗(yàn)點(diǎn)就可以很容易的識(shí)別出來。 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(cè)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。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(hào)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(cè)試 AE信號(hào)的可視化 圖表 11所有測(cè)試 X方向?qū)拵盘?hào)的可視化 圖表 12所有測(cè)試 X方向平均頻率信號(hào)的可視化 圖表 13用模板信號(hào)去進(jìn)行信號(hào)鑒定 1 執(zhí) 行概要 引言 由牛津信號(hào)分析機(jī)構(gòu)組織進(jìn)行的這次調(diào)查的目的是檢驗(yàn)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(shí)的適用性能。 S3retraction (shown in blue) Figure 7 Example signature of variable y plotted against operatingpoint Figure 8 Power spectra for test 51, frequency (Hz) on the xaxis between [0 fs/2] Figure 9 Average significant frequency fu Figure 10 Visualisation of AE signatures for all tests Figure 11 Visualisation of Ax broadband signatures for all tests Figure 12 Visualisation of Ax averagefrequency signatures for all tests Figure 13 Novelty detection using a template signature Figure 14 1 Executive Summary 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 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 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. 2 Introduction Oxford BioSignals Limited This