Faults Detected是什么意思_Faults Detected在线翻译_Faults Detected什么意思_Faults Detected的意思_Faults Detected的翻译_911查询
Faults Detected是什么意思
输入英文单词或中文词语查询其翻译，例如
Faults Detected是什么意思 Faults Detected在线翻译 Faults Detected什么意思 Faults Detected的意思 Faults Detected的翻译 Faults Detected的解释
Faults DetectedFaults Detected 网络解释1. 系统正常& & 11System Checked Okay检查系统正常 | 12No Faults Detected PerformManual Input Checkes系统正常 | 13Fualts Detected Perform Manual Input Checkes系统有记忆故障码Faults Detected 网络例句1. In relation to hardware failures and software faults, detected by the diagnostic tests or through normal operation & &在与硬件失效和软件故障有关时
，通过诊断测试或正常操作发现的。2. In average, there are 5.34% transition faults detected with the longer path if FS is considered. & &在使用功能敏化的方法下，整体平均有5.34%的转变错误可以被较长的路径测试。3. In order to improve product quality and economic benefit, the process conditions should be closely monitored and faults should be timely detected. & &它通过密切地监督生产过程的运行状态，不断地检测过程的变化和故障信息，从而有效地提高了产品质量和经济效益。4. In relation to hardware failures and software faults, detected by the diagnostic tests or through normal operation & &在与硬件失效和软件故障有关时控制工程网版权所有，通过诊断测试或正常操作发现的。5. In order to improve product quality and economic benefit, the process conditions should be closely monitored and faults should be timely detected. & &它通过密切地监督生产过程的运行状态，不断地检测过程的变化和故障信息，从而有效地提。。。6. In relation to hardware failures and software faults
, detected by the diagnostic tests or through normal operation & &在与硬件失效和软件故障有关时
，通过诊断测试或正常操作发现的。7. Faults are detected, analyzed, and located on the basis of protective relay operations and fault recorder`s information. & &故障原因和故障定位的分析需要综合继电保护的动作行为和故障录波器在故障期间的动态过程。Faults Detected是什么意思,Faults Detected在线翻译,Faults Detected什么意思,Faults Detected的意思,Faults Detected的翻译,Faults Detected的解释,Faults Detected的发音,Faults Detected的同义词,Faults Detected的反义词,Faults Detected的例句,Faults Detected的相关词组,Faults Detected意思是什么,Faults Detected怎么翻译,单词Faults Detected是什么意思常用英语教材考试英语单词大全 (7本教材)
出国英语单词大全 (5本教材)
大学英语单词大全 (13本教材)
高中英语单词大全 (6本教材)
初中英语单词大全 (13本教材)
小学英语单词大全 (33本教材)
别人正在查
911查询 全部查询 网址：
(共20个)占卜求签
(共17个)民俗文化
(共15个)交通出行
(共10个)学习应用
(共25个)休闲娱乐
(共10个)站长工具
(共9个)身体健康
&2015 　京ICP备号-6　当前位置： &
with all faults是什么意思
中文翻译不保证商品没有瑕疵不保证商品无瑕疵承担货物一切缺陷责任:&&&&短语和例子 Away with him! 把他带走! D ...:&&&&adj. 1.所有的，全部的，整个的，一切的。 2.非常 ...:&&&&n. 1.过失，过错；罪过，责任。 2.缺点，缺陷，瑕疵 ...
例句与用法1.The tapes are sold with all faults磁带一经售出，概不退换。 2.Microsoft is providing the sql critical update kit as is and with all fault , and hereby disclaims all other warranties and conditions , whether express , implied or statutory , including , but not limited to , any if any implied warranties , duties or conditions of merchantability , of fitness for a particular purpose , of reliability or availability , of accuracy or completeness of responses , of results , of workmanlike effort , of lack of viruses , of lack of negligenceMicrosoft按“原样”包括其所有可能存在的错误提供此sql关键更新工具包，并特此声明不负责其他任何明示隐含或法定的担保和条件，其中包括但不限于下列任何隐含的担保责任或条件如果有：适销性对于特定目的的适用性可靠性或可用性回应的准确性或完整性结果工艺的精良无病毒以及无疏忽。
相邻词汇热门词汇文档分类：
在线文档经过高度压缩，下载原文更清晰。
淘豆网网友近日为您收集整理了关于三相异步电动机故障诊断外文文献翻译、中英文翻译、机械机电毕业外文翻译的文档，希望对您的工作和学习有所帮助。以下是文档介绍：三相异步电动机故障诊断外文文献翻译、中英文翻译、机械机电毕业外文翻译 翻译部分英文原文Fault Diagnosis of Three Phase Induction Motor Using workTechniquesAbstract:Fault diagnosis of induction motor is gaining importance in industry because ofthe need to increase reliability and to decrease possible loss of production due to machinebreakdown.Due to environmental stress and many others reasons different faults occur ininduction motor. Many researchers proposed different techniques for fault detection anddiagnosis.However,many techniques available presently require a good deal of expertise to applythem essfully.Simpler approaches are needed which allow relatively unskilled operators tomake reliable decisions without a diagnosis specialist to examine data and diagnose problems.Inthis paper simple,reliable and economical work(NN)based fault classifier isproposed,in which stator current is used as input signal from motor.Thirteen statisticalparameters are extracted from the stator current and PCA is used to select proper input.Data isgenerated from the experimentation on specially designed 2 Hp,4 pole 50 Hz.three phaseinduction motor.For classification,NNs like MLP,SVM and statistical classifiers based on CARTand Discriminant Analysis are verified.Robustness of classifier to noise is also verified onunseen data by introducing controlled Gaussian and Uniform noise in input and output.Index Terms: Induction motor, Fault diagnosis, MLP, SVM,CART, Discriminant Analysis,PCAI.INTRODUCTIONINDUCTION motors play an important role as prime movers in manufacturing,processindustry and transportation due to their reliability and simplicity in construction.In spite of theirrobustness and reliability,they do occasionally fail,and unpredicted downtime is obviously costlyhence they required constant attention.The faults of induction motors may not only cause theinterruption of product operation but also increase costs,decrease product quality and affect thesafety of operators.If the lifetime of induction machines was extended, and efficiency ofmanufacturing lines was improved,it would lead to smaller production expenses and lower pricesfor the end user.In order to keep machines in good condition, some techniques i.e.,faultmonitoring, fault detection, and fault diagnosis have e increasingly essential.The mon faults of induction motors are bearing failures, stator phase winding failures ,brokenrotor bar or cracked rotor end-rings and air-gap irregularities.The objective of this research is to develop an alternative work based incipientfault-detection scheme that e the limitations of the present schemes in the sensethat,they are costly, applicable for large motors, furthermore many design parameters arerequested and especially concerning to long time operating machines, these parameters cannot beavailable easily.pared to existing schemes, proposed scheme is simple, accurate, reliableand economical. This research work is based on real time data and so proposed workbased classifier demonstrates the actual feasibility in a real industrial situation. Four work structures are presented in this paper with all kinds of performances and about100%classification accuracy is achieved.II.FAULT CLASSIFICATION USING NNThe proposed fault detection and diagnosis scheme consists of four procedures as shown inFig.1:1. Data collection & acquisition2. Feature extraction3. Feature selection4. Fault classificationA. Data Collection and Data acquisitionIn this paper the mon faults namely stator winding interturn short(I),rotor entricity(E)and both of them(B)are considered.Fig.1.General Block Diagram of proposed classifierFor experimentation and data generation the specially designed 2 HP, three phase,4pole,415V,50 Hz induction motor is selected. Experimental set up is as shown in Fig.2.Fig.2.Experimental SetupThe load of the motor was changed by adjusting the spring balance and belt.Three ACcurrent probes were used to measure the stator current signals for testing the fault diagnosissystem. The maximum frequency of used signal was 5 kHz and the number of sampled data was2500.From the time waveforms of stator currents as shown in Fig.3,no conspicuous differenceexists among the different conditions.Fig.3.Experimental Waveforms of Stator currentB. Feature ExtractionThere is a need e up with a feature extraction method to classify faults.In order toclassify the different faults,the statistical parameters are used.To be precise, ‘sample’ statisticswill be calculated for current data.Overall thirteen parameters are calculated as input featurespace.Minimum set of statistics to be examined includes the root mean square (RMS)of the zeromean signal(which is the standard deviation),the maximum, and minimum values the skew nesscoefficient and kurtosis coefficient. Pearson’s coefficient of skew ness, 2g defined by:xSxxg~32 (1)Where x denotes mean, x denotes median and xS denotes the sample standarddeviation.The sample coefficient of variatxSvxr (2)The thr sample moment about the sample mean for a nxxmrniir1)((3)m2 denotes spread about the center,m3 refers to skewnm4 denotes howmuch data is massed at the center. Second,third and fourth moments are used to define thesample coefficient of skewness, 3g and the sample coefficient of kurtosis, 4g as follows. 3233mmg
(4) 4244mmg
(5)The sample covariance between dimensions j)1())((ikikjijjk(6)The ordinary correlation coefficient for dimensions j and k ,kjjkjkSScr (7)C. Feature SelectionBefore a feature set is fed into a classifier,most superior features providing dominantfault-related information should be selected from the feature set,and irrelevant or redundantfeatures must be discarded to improve the classifier performance and avoid the curse ofdimensionality.Here ponent Analysis(PCA)technique is used to select the mostsuperior features from the original feature set.ponents(PCs)puted byPearson rule.The Fig.4 is related to a mathematical object,the eigenvalues,which reflect thequality of the projection from the 13-dimensional to a lower number of dimensions.Fig.4.ponent, Eigenvalues and percent variabilityD. Fault Classifier(1)MLP NN Based ClassifierSimple Multilayer Perceptron(MLP)work is proposed as a fault classifier.FourProcessing Elements are used in output layer for four conditions of motor namely Healthy, Interturn fault,Eccentricity and Both faults. From results as shown in Fig.5,five PCAs ahence number of PEs in input layer is five.Fig.5(a).Variation of Average MSE on training and CV with number of PCs as inputFig.5(b).Variation of Average Classification Accuracy on Testing on Testdata, Training data and CV data with number of PCs as inputThe randomized data is fed to the work and is retrained five times with differentrandom weight initialization so as to remove biasing and to ensure true learning andgeneralization for different hidden layers.This also removes any affinity or dependence of choiceof initial connection weights on the performance of NN.It is observed that MLP with a singlehidden layer gives better performance.The number of Processing Elements(PEs)in the hiddenlayer is varied.work is trained and minimum MSE is obtained when 5 PEs are used inhidden layer as indicated in Fig.6.Fig.6.Variation of Average MSE with number of PEs in Hidden LayerVariousTransferfunctions,namely,Tanh,Sigmoid,Liner-tanh,Linear-sigmoid,Softmax,Biasaxon, Linear axon and learning rules, namely, Momentum, Conjugate-Gradient, QuickPropagation, Delta Bar Delta, and Step are verified for training, cross validation andtesting.Minimum MSE and average classification accuracy on training and CV data set pared . With above experimentations finally,the MLP NN classifier is designed withfollowing specifications,Number of Inputs:5;Number of Hidden Layers:01;Number of PEs in Hidden Layer:04;Hidden Layer:Transfer function:tanh Learning Rule:MomentumStep size:0.6 Momentum:0.5Output Layer:Transfer function:tanh Learning Rule:MomentumStep size:0.1 Momentum:0.5Number of connection weights:44Training time required per epoch per exemplar:0.0063 ms(2) SVM NN Based ClassifierThe support vector machine(SVM)is a new kind of classifier that is motivated by twoconcepts. First , transforming data into a high-dimensional space can plexproblems (plex decision surfaces)into simpler problems that can use linear discriminantfunctions. Second, SVMs are motivated by the concept of training and using only those inputsthat are near the decision surface since they provide the most information about the classification.It can be extended to multi-class.SVMs training always seek a global optimized solution andavoid over fitting,so it has ability to deal with a large number of feature.Generalized Algorithm for the classifier:For N dimensional space data ix (i =1…N) this algorithm can be easily extended work by substituting the inner product of patterns in the input space by the kernel function,leading to the following quadratic optimization problem:
NiNjjijijiNiixxGddJ1 121)2,(21)(
(8)Subject to01Niiid
Nii, . . .1,0
2,xG represents a Gaussian function, N is the number of samples, i are a set ofmultipliers(one for each sample),NijijjiibxxGddxJ12))2,(()(
(10)and)(m i n iixgM
(11)and choose mon starting multiplier i ,learning rate
, and a small threshold. Then, whileM&t, we choose a pattern ix and calculate an update ))(1( iixg
and perform the updateIf 0)(
iin )()()1( nnn iii iidnbnb
)()1( (12)And if 0)(
iin )()1( nn ii )()1( nbnb
(13)After adaptation only some of the i are different from zero (called the support vectors). It iseasy to implement the kernel Adatron algorithm since )( ixg can puted locally to eachmultiplier,provided that the desired response is available in the input file.In fact,the expressionfor )( ixg resembles the multiplication of an error with an activation,so it can be included in theframework of work learning.The Adatron algorithm essentially prunes the work so that its output for testing is given by,))2,(s g n ()(2Nv e c t o r ss p p o r tiiiiibxxGdxf
(14)And cost function in error criterion is12) ) ) )(,( t a n h ()((21)(iitytdtJ (15)Number of PCs as input and step size is selected by checking the average minimum MSE andaverage cla results are shown in Fig 7.Fig.7(a).Variation of Average MSE on training and CV with numberof PCs as inputFig.7(b).Variation of Average Classification Accuracy on Testing on Testdata,Training data and CV data with number of PCs as inputFinaly the SVM based classifier is designed with following specifications,Number of Inputs:5; Step Size:0.7Time required per epoch per exemplar:0.693 msNumber of connection weights:264Designed classifier is trained and tested using the similardatasets and results are as shown in Fig.8 and Fig.9Fig.8.Variation of Average Minimum MSE on Testing on Test data,CVdata and Training data with number of rows shifted(n)Fig.9.Variation of Average Minimum MSE on Training and CV withvarious groups(3)Classification and Regression Trees(CART)CART induces strictly binary trees through a process of binary recursively partitioning offeature space of a data set. The first phase is called tree building,and the other is treepruning.Classification tree is developed using XLSTAT-2009.Various methods, measures andmaximum tree depth are checked and results are shown in Fig.10.It is observed that optimumaverage classification accuracy on testing on test data and CV data is found to be 90.91 and 80percent,respectively.Fig.10(a).Variation of Average Classification Accuracy on Testing onTest data and CV data with Method and Measure of TreesFig.10(b).Variation of Average Classification Accuracy on Testing onTest data and CV data with Depth of Trees(4) Discriminant AnalysisDiscriminant analysis is a technique for classifying a set of observations into predefinedclasses.The purpose is to determine the class of an observation based on a set of variables knownas predictors or input variables.The model is built based on a set of observations for which theclasses are known. Based on the training set,the technique constructs a set of linear functions ofthe predictors,known as discriminant functions ,such that cxbxbxbL nn ...2211, wherethe sb
are discriminant coefficients, the sx
are the input variables or predictors and c is aconstant. Discriminant analysis is done using XLSTAT-2009.Various models are checked andresults are shown in Fig.11.It is observed that optimum average classification accuracy on testingon test data and CV data is found to be 91.77 and 80 percent,respectively.Fig.11.Variation of Average Classification Accuracy on Testing on Testdata and CV data with Model of DAIII.NOISE SUSTAINABILITY OF CLASSIFIERSince the proposed classifier is to be used in real time,where measurement noise isanticipated,it is necessary to check the robustness of classifier to noise.To check the robustness,Uniform and Gaussian noise with mean value zero and variance varies from 1 to 20%isintroduced in input and output and average classification accuracy on testing data i.e.unseen datais checked.It is seen that SVM based classifier is the most robust classifier in the sense that it cansustain both uniform and Gaussian noise with 14%and 20%variance in input and output,respectively. Results are as shown in Table IG-Gaussian NoiseU-Uniform NoiseIV.RESULTS AND DISCUSSIONIn this paper,the authors evaluated the performance of the developed ANN based classifiersfor detection of four fault conditions of three phase induction motor and examined theresults.MLP NN,and SVM are optimally designed and pletion of the training,work is tested to detect different types of faults. Similarly step size is varied in SVMand 0.7 step size is found to be optimum. These confirm our idea that the proposed featureselection method based on the PCA can select the most superior features from the originalfeature set,and therefore,is a powerful feature selection method.Also proposed classifier isenough robust to the noise,in the sense that classifier gives satisfactory results for Uniform andGaussian noise with 14%variance in input and with 20% variance in parative resultsare shown in Fig.12 and Table II.Fig.parative analysis of various classifier w.r.t.Averageclassification accuracy.TABLE PARATIVE RESULTS OF NN BASED CLASSIFIERS播放器加载中，请稍候...
该用户其他文档
下载所得到的文件列表三相异步电动机故障诊断外文文献翻译、中英文翻译、机械机电毕业外文翻译.doc
文档介绍：
三相异步电动机故障诊断外文文献翻译、中英文翻译、机械机电毕业外文翻译 翻译部分英文原文Fault Diagnosis of Three Phase Induction Motor Using workTechniquesAbstract:Fault diagnosis of induction motor is gaining importance in industry because ofthe need to increase reliability and to decrease possible lo...
内容来自淘豆网转载请标明出处.
浏览：13次电子信息专业中英文文献翻译对照_百度文库
两大类热门资源免费畅读
续费一年阅读会员，立省24元！
评价文档：
&&¥10.00
&&¥10.00
&&¥15.00
&&¥10.00
喜欢此文档的还喜欢
电子信息专业中英文文献翻译对照
本​文​章​适​用​于​各​种​毕​业​论​文​需​要​的​外​文​翻​译
阅读已结束，如果下载本文需要使用
想免费下载本文？
把文档贴到Blog、BBS或个人站等：
普通尺寸(450*500pix)
较大尺寸(630*500pix)
你可能喜欢当前位置： &
pastern是什么意思
中文翻译英英解释百科解释n.(马足的)?。
例句与用法1.There should be very little bend of pastern踝部可能会有稍微的弯曲。 2.Are pasterns firm and almost perpendicular to the ground跗关节是否坚固并且几乎垂直地面。 3.The pasterns are short and strong掌骨短而强壮。 4.A small dainty foot or one down at the pastern are not functional小而秀气的脚或者弯曲过大的踝部是没有功能的。 5.Pasterns show flexibility with a slight slope when viewed from the side从侧面看前驱与背线的过度阶段衔接适度。 6.The pastern mold adopts wearable and heat - resistant silica gel , the aluminium mold can come to 200胶模采用耐磨热硅胶;外铝模温度可达200 7.Viewed from the side , pasterns are slightly slanted , with the pastern joint strong , but flexible从侧面看，球节略微倾斜，坚固，但是是灵活的。 8.Faults : weak pasterns ; too narrow or to out at the elbows缺点（不合格） ：球节无力；过重的骨量；前面看上去过窄或过宽；肘关节外翻。 9.Well laid shoulders , muscular and strongly boned . straight legs with strong , slightly sloping pasterns自然向上的肩部，肌肉发达，骨骼强壮。腿部笔直强壮，足弯部稍上翻。 10.From the cool shadow of the doorway he saw the horses pass parliament street , harness and glossy pasterns in sunlight shimmering他从门道的荫凉处瞧见马队正经过议会街，挽具和润泽光滑的马脚在太阳映照下闪闪发着光。 &&更多例句：&&1&&
相邻词汇热门词汇}