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機電工程學院 畢業(yè)設計外文資料翻譯 設計題目: JD1041載貨汽車變速器及傳動軸設計 譯文題目: 汽車變速箱故障診斷之噪聲測量 學生姓名： 學 號： 專業(yè)班級： 指導教師： 正文：外文資料譯文 附 件：外文資料原文 指導教師評語： 譯文基本順暢、正確，專業(yè)內容理解和翻譯基本符合原文，達到要求。 簽名：張月英2016 年 4 月 6 日 外文資料譯文（出處：能源和環(huán)境國際期刊） 汽車變速箱故障診斷之噪聲測量 Sameh M. Metwalley, Nabil Hammad, Shawki A. Abouel-Seoud 哈勒旺大學工學院，埃及開羅 摘要 噪聲測量是許多健康監(jiān)測和診斷旋轉機器技術之一，例如變速箱。雖然關于理解潛在噪音測量監(jiān)控變速箱的重要研究一直在進行，但這只應用于任何類型的齒輪(正齒齒輪，螺旋等等)。實驗室規(guī)模的狀態(tài)監(jiān)測、單級齒輪箱，代表了汽車真正的齒輪箱，使用非破壞性檢驗方法獲得的處理波形與先進的信號處理技術是當前工作的目的。聲發(fā)射是用于這一目的。實驗裝置和儀器存在于細節(jié)。重點給出了通過噪聲測量信號的處理來提取傳統(tǒng)以及小說從監(jiān)測波形參數(shù)可能的診斷價值。參數(shù)的進化特性和測試時間選擇,突出顯示評估和參數(shù)是最有趣的診斷行為。目前的工作還反應了包括長期(0--6.0小時)實驗叛逃齒輪系統(tǒng)的結果,橫向削減范圍從0.75毫米到3.0毫米模擬牙齒裂縫。不同的參數(shù)記錄來自聲發(fā)射的信號相關的分析,探討其診斷價值狀態(tài)監(jiān)測系統(tǒng)的發(fā)展。 版權?2011年國際能源和環(huán)境基礎,保留所有權利。 關鍵詞:診斷、齒輪傳動系統(tǒng)、聲壓級,平穩(wěn)信號,故障設備,測量設備,裝備條件、監(jiān)控、維護行動。 1、介紹 聲發(fā)射是指范圍導致結構傳遞的生成和fluid-borne(液體、氣體)傳播波由于快速釋放來源于材料表面或局部的能量的現(xiàn)象。聲發(fā)射技術在研究和工業(yè)的應用是證據(jù)確鑿的。關于變速箱，幾個調查人員評估了應用聲發(fā)射技術于診斷和預后。其他的應用在聲發(fā)射檢測在直齒圓柱齒輪彎曲疲勞多指出聲發(fā)射比振動裂紋擴展和剛度測量更敏感。同樣,AE被發(fā)現(xiàn)比振動分析表面破壞的規(guī)模更敏感。 2、平穩(wěn)信號數(shù)據(jù)分析 文獻中機械的故障診斷系統(tǒng)有許多信號處理技術。Case-dependent知識和調查需要選擇合適的信號處理工具。在條件最常見的是波形數(shù)據(jù)監(jiān)測振動信號和聲學排放。其他超聲信號波形數(shù)據(jù),電動機電流、局部放電等。在文獻中,有兩個主要類別的固定波形數(shù)據(jù)分析，時域分析和頻域分析。 3、時域分析 時域分析是直接基于波形本身的時間計算特征的描述性統(tǒng)計如波形信號。傳統(tǒng)的時域分析的意思是峰間隔,標準差,波峰因素和高階統(tǒng)計量(均方根,偏態(tài)、峰態(tài)等)。這些特性通常稱為時域特性。一個受歡迎的時域分析方法是時間同步平均(TSA)。TSA的想法是使用原始信號的演進統(tǒng)計平均值,以消除或減少噪音和影響。 更先進的方法是時域波形數(shù)據(jù)分析應用時間序列模型，時間序列模型的主要思想是適應波形數(shù)據(jù)參數(shù)模型和提取時間基于該參數(shù)模型特性。流行的模型在文獻中使用汽車回歸(AR)模型和汽車回歸移動平均(ARMA)模型。 在本文中,只有高階統(tǒng)計量的均方根(RMS)。這個功能通常被稱為時域特性。RMS是一種平均的信號,對于離散信號,RMS值定義為： 4、頻域分析 頻域分析是基于轉換后的信號。頻域分析比時域分析的優(yōu)勢是能夠輕易識別和隔離某些感興趣的頻率成分。最廣泛使用的光譜分析是傅里葉變換(FFT)的意思。頻譜分析的主要思想是看看整個光譜或仔細看看感興趣的某些頻率成分,從而提取特征信號。 5、常數(shù)比例帶寬(CPB) 基本常數(shù)之間的選擇,絕對帶寬和恒定的比例(百分比)帶寬是一個固定的百分比調中心頻率。常數(shù)比例帶寬用統(tǒng)一的標準決議一個線性頻率刻度,這例如,以相同的分辨率和諧波相關組件,分離促進檢測諧波模式。然而,線性頻率刻度制約了有用的頻率范圍的限制(最)二十年。 值得特別重視的兩個特殊類型的常數(shù)比例帶寬濾波器,即八度和第三個八度過濾器。因為這些被廣泛使用,尤其是聲學測量。這樣的上極限頻率通常是兩倍有限的頻率較低,導致70.0%的帶寬。 6、測量系統(tǒng)和測試過程 圖1：實驗裝置 圖2：B&K便攜式多通道脈沖 圖3：B&K脈沖labshop 圖4：齒裂現(xiàn)象 圖1顯示了用于齒輪箱測試的實驗裝置。齒輪箱由兩個2毫米,壓力角20°,64和26個牙齒,寬40毫米的螺旋齒輪模塊。齒輪的軸是由兩個球軸承支持。為了確保適當?shù)臐櫥麄€系統(tǒng)是固定在一個油池中。變速器是由一個電動馬達和電力液壓盤式制動器,而速度是衡量照片電探針。布魯&卡亞爾:(b和k)便攜式多通道脈沖型3560 - b - x05(圖2)與冷凝器1/2 -麥克風和前置放大器類型4189 - 4189在變速箱前殼的中心位置遠離套管和地面分別由1.0米和0.50米。b和k脈沖labshop是測量軟件使用7700型分析結果(圖3)。各種參數(shù)進化在測試期間——從一個代表性的測試裝置系統(tǒng)根厚度的減少模擬齒裂紋(圖4)將和詳細的研究。許多測試進行相同的配置行為產(chǎn)生類似的參數(shù)。人為地用鐵絲電火花加工齒輪的齒的根源來創(chuàng)建一個壓力濃度,最終導致了傳播裂紋。裂縫深度范圍從0.75毫米到3.0毫米，厚度幾乎0.5毫米。錄音每15分鐘被收錄,總共24段錄音(0--6.0 h測試時間),直到終止測試。這種類型的測試首選是為了有機會監(jiān)視浴破壞模式,即自然裂縫傳播。損害是保證通過增加測試周期的剩余的金屬的地方牙區(qū)域有足夠的壓力在塑性變形區(qū)域。仔細監(jiān)測SPL反應揭示了一些微妙的變化和增加反應。在最初的幾小時，或者“磨合期”，當減小負載,所有的反應都略有下降。磨合期之后是長期與很少或沒有響應的變化,即穩(wěn)定時期。最后,常常在失敗之前幾個小時,普遍認為反應減少的散度期。 表1：齒輪和齒輪輪規(guī)范 號碼 參數(shù) 齒輪 小齒輪 1 齒數(shù) 64 26 2 模數(shù) 2 2 3 正常壓力角 200 200 4 軸角 900 900 5 齒頂間隙 0.25mm 0.25mm 6 齒頂高 2mm 2mm 7 全齒高 4.5mm 4.5 8 材料 鋼 鋼 表2：齒輪各種缺陷的細節(jié) 號碼 齒輪 故障描述 尺寸/毫米 1 運行齒輪 齒輪健康 0.75×0.5×40 2 齒輪1 齒根裂紋 1.5×0.5×40 3 齒輪2 齒根裂紋 2.25×0.5×40 4 齒輪3 齒根裂紋 3.0×0.5×40 表1中提到的五個齒輪與一個小齒輪的使用細節(jié)。一個是新的輪,被認為是無缺陷。在其他四個齒輪創(chuàng)建缺陷，并使用電火花控制缺陷的大小。表2中描述了各種缺陷的細節(jié)及其視圖如圖4所示。裂縫的大小大于1mm在實際情況中遇到。允許系統(tǒng)的初始運行一段時間后，聲壓級信號從安裝在測試結構前面的麥克風收入。 表2記錄裂紋大小(g4),每15分鐘被收錄,總共24段錄音(0--6.0 h測試持續(xù)時間)的結果,直到終止測試。這種類型的測試是首選是為了有機會來監(jiān)控浴破壞模式,即自然的裂紋擴展。損害是保證通過增加剩余的金屬牙區(qū)足夠的壓力在塑性變形區(qū)域測試時間的。監(jiān)測SPL反應揭示了一些微妙的變化和增加反應。 7、結果與討論 在圖5中,健康齒輪速度是每分鐘400轉,負載是10 Nm,在時間域(圖5)頻域(圖5 b)聲壓級(SPL)以1.0米的位置遠離變速箱。這表明高水平在200 Hz - 300,400Hz - 500 Hz,600 Hz - 700Hz(圖5 b)的頻率范圍,而其余頻率較低的水平幾乎不變。測量上的負載的影響:在400 rpm的速度提出了（圖6）,1/3-octave SPL增加隨著負載的增加而變小。 （a）時域聲壓基譜 （b）頻域聲壓基譜 圖5：聲壓級譜 8、結論 1 -實驗方法可以用于診斷能力開發(fā)的這項工作。此外,明顯的周期性脈沖引起的裂縫牙齒出現(xiàn)在時域和頻域，平均在1.3倍頻率信號，隨著裂縫水平的增加,這些提取特征的重要的診斷信息中牙齒裂縫損傷。 2-FFT技術和高階統(tǒng)計量的RMS反映聲壓級(SPL) 這可以成為一個有效的方法來進行預測性維護變速箱。因此經(jīng)濟而又有很好的發(fā)展前景。 3 -識別變速箱噪聲的SPL。當應用于齒輪箱,可準確反應齒輪的狀態(tài)。甚至從齒輪測試中得到真實的數(shù)據(jù)。結果看起來有前途。此外,提出了噪聲的聲壓級別(SPL)簽名的方法在其他測試平臺進行測試。RMS平均值分析可能是一個良好的故障早期檢測和表征指標。 4 -為了研究人工誘導裂縫損害變速箱的發(fā)展，幾個小時或幾天使用噪聲進行了測試和記錄了SPL監(jiān)控,得到的均方根平均計算。在錄音中,錄音RMS值的轉換時間突出顯示反映出變速箱關鍵操作的變化。 16 附件：（外文資料原文） Vehicle gearbox fault diagnosis using noise measurements Sameh M. Metwalley, Nabil Hammad, Shawki A. Abouel-Seoud Faculty of Engineering, Helwan University, Cairo, Egypt. Abstract Noise measurement is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of noise measurement in monitoring gearboxes this has been solely applied on any types of gears (spur, helical, ..etc.). The condition monitoring of a lab-scale, single stage, gearbox, represents the vehicle real gearbox, using non-destructive inspection methodology and the processing of the acquired waveform with advanced signal processing techniques is the aim of the present work. Acoustic emission was utilized for this purpose. The experimental setup and the instrumentation are present in detail. Emphasis is given on the signal processing of the acquired noise measurement signal in order to extract conventional as well as novel parameters potential diagnostic value from the monitoring waveform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with most interesting diagnostic behavior are highlighted. The present work also reports the results concluded by long term (~ 6.0 h) experiments to a defected gear system, with a transverse cuts ranged from 0.75 mm to 3.0 mm to simulate the tooth crack. Different parameters, related by the analysis of the recording signals coming from acoustic emission are presented and their diagnostic value is discussed for the development of a condition monitoring system. Copyright ? 2011 International Energy and Environment Foundation - All rights reserved. Keywords: Diagnostic, Geared system, Sound pressure level, Stationary signal, Faulty gear, Measuring devices, Condition of gear, Monitoring, Maintenance action. 1. Introduction Acoustic emission is defined as the range of phenomena that results in the generation of structure-borne and fluid-borne (liquid, gas) propagating waves due to the rapid release of energy from localised sources within and/or on the surface of a material. The application of the acoustic emission technology in research and industry is well-documented. In relation to gearboxes, a few investigators have assessed the application of acoustic emission technology for diagnostic and prognostic purposes. Others applied acoustic emission in detecting bending fatigue on spur gears and noted that acoustic emission is more sensitive to crack propagation than vibration and stiffness measurements. Again, AE was found to be more sensitive to the scale of surface damage than vibration analysis. 2. Stationary signal data analysis There are numerous signal processing techniques in the literature for fault diagnostics of mechanical systems. Case-dependent knowledge and investigation are required to select appropriate signal processing tools among a number of possibilities. The most common waveform data in condition monitoring are vibration signals and acoustic emissions. Other waveform data are ultrasonic signals, motor current, partial discharge, etc. In the literature, there are two main categories of stationary waveform data analysis; time-domain analysis and frequency-domain analysis. 3. Time-domain analysis Time-domain analysis is directly based on the time waveform itself. Traditional time-domain analysis calculates characteristic features from time waveform signals as descriptive statistics such as mean, peak, peak-to-peak interval, standard deviation, crest factor and high order statistics (root mean square, skewness, kurtosis, etc.). These features are usually called time-domain features. A popular time-domain analysis approach is Time Synchronous Average (TSA). The idea of TSA is to use the ensemble average of the raw signal over a number of evolutions in an attempt to remove or reduce noise and effects from other sources to enhance the signal components of interest. More advanced approaches of time-domain analysis apply time series models to waveform data. The main idea of time series modelling is to fit the waveform data to a parametric time model and extract features based on this parametric model. The popular models used in the literature are the Auto Regressive (AR) model and the Auto Regressive Moving Average (ARMA) model. In this paper, only high order statistic of root mean square (RMS) is used. This feature is usually called time-domain features. RMS is a kind of average of signal, for discrete signals, the RMS value is defined as: 4. Frequency-domain analysis Frequency-domain analysis is based on the transformed signal in frequency domain. The advantage of frequency–domain analysis over time-domain analysis is its ability to easily identify and isolate certain frequency components of interest. The most widely used conventional analysis is the spectrum analysis by mean of fast Fourier transform (FFT). The main idea of spectrum analysis is to either look at the whole spectrum or look closely at certain frequency components of interest and thus extract features from the signal . 5. Constant percentage bandwidth (CPB) The basic choice to be made is between constant absolute bandwidth and constant proportional (percentage) bandwidth where the absolute bandwidth is a fixed percentage of the tuned centre frequency. Constant percentage bandwidth gives uniform resolution on a linear frequency scale, and this for example, gives equal resolution and separation of harmonically related components and this will facilitate detection of a harmonic pattern. However, the linear frequency scale automatically gives a restriction of the useful frequency range to (at the most) two decades. It is worth paying particular attention to two special classes of constant percentage bandwidth filter, viz. octave and third octave filters since these are widely used, in particular for acoustic measurements. The former have a bandwidth such that the upper limiting frequency of the pass band is always twice the lower limited frequency, resulting in the band width of 70.0%. 6. Measuring system and test procedure Figure 1 shows the experimental setup used for the gearbox testing. The gearbox consists of two helical gears with a module of 2 mm, pressure angle 20°, which have 64 and 26 teeth with 40 mm face width. The axes of the gears are supported by two ball bearings each. The entire system is settled in an oil basin in order to ensure proper lubrication. The gearbox is powered by an electric motor and consumes its power on a hydraulic disc brake, while the speed is measured by photo electric probe. Bruel & Kjaer (B&K) portable and multi-channel PULSE type 3560-B-X05 (Figure 2) with condenser 1/2- microphone and preamplifier type 4189A-021 was positioned in the center of gearbox front casing away from the casing and the ground by 1.0 m and 0.50 m respectively [13]. The B&K PULSE labshop is the measurement software type 7700 is used to analyse the results (Figure 3). In terms of various parameters evolution during the test – from a representative test on a gear system with a cut of root thickness to simulate the tooth crack (Figure 4) will be presented and detailed in this study. Many tests were conducted on the same configuration yield similar parameters behaviour. Small cracks were made artificially with wire electrical discharge machining at the root of gear of one tooth to create a stress concentration which eventually led to a propagating crack. The crack depths are ranged from 0.75 mm to 3.0 mm with thickness of almost 0.5 mm. Recordings every 15 min were acquired and a total of 24 recordings (~ 6.0 h of test duration) were resulted until the termination of the test. This type of test was preferred in order to have the opportunity to monitor bath damage modes, i.e., the natural crack propagation. Damage is assured by increasing the test period to the point of where the remaining metal in the tooth area has enough stress to be in the plastic deformation region. Careful monitoring of the SPL responses reveals some subtle and increasing changes in responses. When the gear tooth is brought under load, all the response are seen declining slightly over initial few hours, or 'break-in period'. Break-in period is followed by a long period with little or no change in the responses, 'or stable period'. Finally, often several hours prior to failure, one generally sees the responses decrease during the 'divergence period'. Five gear wheels with one pinion whose details mentioned in Table 1 have been used. One was a new wheel and was assumed to be free from defects (go). In the other four gear wheels, defects were created using EDM in order to keep the size of the defect under control. The details of the various defects are depicted in Table 2 and its view is shown in Figure 4. The size of cracks is a little bigger than one can encounter in the practical situation. The sound pressure level signal from the microphone mounted on front of the test structure was taken, after allowing initial running of the system for sometime. At crack size (g4), Table 2, recordings every 15 min were acquired and a total of 24 recordings (~ 6.0 h of test duration) were resulted until the termination of the test. This type of test was preferred in order to have the opportunity to monitor bath damage modes, i.e., the natural crack propagation. Damage is assured by increasing the test period to the point of where the remaining metal in the tooth area has enough stress to be in the plastic deformation region. Careful monitoring of the SPL responses reveals some subtle and increasing changes in responses. 7. Results and discussion In Figure 5, where the speed is 400 rpm, and load is 10 Nm for healthy gear, the sound pressure level (SPL) measured at a location of 1.0 m away from the gearbox face in time domain (Figure 5a) and in frequency domain (Figure 5b). This indicates high levels in the frequency ranges of 200 Hz-300 Hz, 400 Hz-500 Hz and 600Hz-700 Hz (Figure 5b), while the levels of the remaining frequency are lower and almost constant. The influence of the load on the measured SPLs at speed of 400 rpm is presented in Figure 6, where the 1/3-octave SPL is increased with the increase of the load dispite some small discrepancies exsited in the 1/3-octaves up to 63 Hz (Figure 6b). This may be attributed to the influnce of gear meshing frequencies, rotating shafts frequencies and structure rigidity resonance frequencies. 8. Conclusion 1- The experimental methodology capability developed in this work could be utilized for diagnostic regime. Furthermore, the obvious periodical impulses caused by the cracked tooth appear in time history, frequency domain and in 1.3-octave band averages signals as the crack level increases, these carry diagnostic information which is important for extracting features of tooth crack damage. 2- The FFT technique and the high order statistic of RMS reflect in the Sound pressure level (SPL) responses of the gearbox. This can be an effective way to carry out the predictive maintenance regime and consequently to save money and look promising. 3- The identification of gearbox noise in terms of SPL is introduced. When applied to the gearbox, the method resulted in an accurate account of the state of the gear, even, when applied to real data taken from the gear test. The results look promising. Moreover, the proposed noise in terms of sound pressure level (SPL) signature methodology has to be tested on the other test rig also. RMS average value analysis could be a good indicator for early detection and characterization of faults. 4- In order to study the development of damage in artificially induced cracks in the gearbox, multi-hour tests were conducted and recordings were acquired using noise in terms of SPL monitoring, where the RMS average was calculated. In the recordings, the transitions in the RMS values with the recording time were highlighted suggesting critical changes in the operation of the gearbox. References [1] Singh A, Houser DR. and Vijayakar S. "Detecting gear tooth breakage using acoustic emission: a feasibility and sensor placement study" J Mech Design Trans. ASME 1999;121(4):587–93, 1999. [2] Miyachika K., Zheng Y. and Tsubokura K. "Acoustic Emission of bending fatigue process of supercarburized spur gear teeth" Progress in Acoustic Emission XI. Anonymous. The Japanese Society for NDI; 2002. pp. 304 310, 2002. [3] Singh, A., Houser, D.R. and Vijayakar, S. 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