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半导体缺陷解析及中英文术语一览一、半导体缺陷1.位错：位错又可称为差排（英语：dislocation），在材料科学中，指晶体材料的一种内部微观缺陷，即原子的局部不规则排列（晶体学缺陷）。
从几何角度看，位错属于一种线缺陷，可视为晶体中已滑移部分与未滑移部分的分界线，其存在对材料的物理性能，尤其是力学性能，具有极大的影响。
产生原因：晶体生长过程中，籽晶中的位错、固-液界面附近落入不溶性固态颗粒，界面附近温度梯度或温度波动以及机械振动都会在晶体中产生位错。
在晶体生长后，快速降温也容易增殖位错。
（111）呈三角形；（100）呈方形；（110）呈菱形。
2.杂质条纹：晶体纵剖面经化学腐蚀后可见明、暗相间的层状分布条纹，又称为电阻率条纹。
杂质条纹有分布规律，在垂直生长轴方向的横断面上，一般成环状分布；在平行生长轴方向的纵剖面上，呈层状分布。
反映了固-液界面结晶前沿的形状。
产生原因：晶体生长时，由于重力产生的自然对流和搅拌产生的强制对流，引起固-液界近附近的温度发生微小的周期性变化，导致晶体微观生长速率的变化，或引起杂质边界厚度起伏，一截小平面效应和热场不对称等，均使晶体结晶时杂质有效分凝系数产生波动，引起杂质中杂质浓度分布发生相应的变化，从而在晶体中形成杂质条纹。
解决方案:：调整热场，使之具有良好的轴对称性，并使晶体的旋转轴尽量与热场中心轴同轴，抑制或减弱熔热对流，可以使晶体中杂质趋于均匀分布。
采用磁场拉晶工艺或无重力条件下拉晶可以消除杂质条纹。
3.凹坑：晶体经过化学腐蚀后，由于晶体的局部区域具有较快的腐蚀速度，使晶体横断面上出现的坑。
腐蚀温度越高，腐蚀时间越长，则凹坑就越深，甚至贯穿。
4.空洞：单晶切断面上无规则、大小不等的小孔。
产生原因：在气氛下拉制单晶，由于气体在熔体中溶解度大，当晶体生长时，气体溶解度则减小呈过饱和状态。
如果晶体生长过快，则气体无法及时从熔体中排出，则会在晶体中形成空洞。
5.孪晶：使晶体断面上呈现金属光泽不同的两部分，分界线通常为直线。
第38卷第2期电力系统保护与控制Vol.38 No.2 2010年1月16日 Power System Protection and Control Jan.16, 2010 过渡电阻对故障暂态分量的影响分析王兴国1, 黄少锋1,2（1.华北电力大学电力系统保护与动态安全监控教育部重点实验室，北京 102206;2.北京四方继保自动化股份有限公司， 北京 100085）摘要： 线路故障后产生大量的暂态分量，会影响常规保护的动作性能。
利用分布参数等值超高压长距离输电线路，重点分析了过渡电阻对暂态高频分量的影响，分别讨论了相间短路和接地故障时，暂态高频分量与过渡电阻之间的关系。
理论分析表明：故障暂态高频分量在频域上表现为一系列固有频率的形式，固有频率受过渡电阻的影响，固有频率的衰减系数与过渡电阻成反比，接地故障时，暂态高频分量中频率最低者与过渡电阻无关,只与电源侧反射系数和线路长度有关。
利用PSCAD 进行了仿真验证，仿真结果与理论分析相符。
关键词: 继电保护； 过渡电阻； 暂态分量； 固有频率Impact of fault resistance for fault transient componentWANG Xing-guo1, HUANG Shao-feng1,2(1.Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education, NorthChina Electric Power University, Beijing 102206, China; 2.Beijing Sifang Automation Co., Ltd, Beijing 100085, China) Abstract: The transient component generated by faults of transmission line affects the performance of protection. Distributed parameter model is used to express EHV transmission lines and impact of fault resistance for transient component is analyzed. The relation between transient component and fault resistance is discussed. Theory analysis shows that the fault component is a series of natural frequencies in frequency domain, when grounding fault occurred, the lowest one of natural frequencies does not impact by fault resistance and the attenuation coefficient increase with fault resistance. Simulation is carried out on PSCAD system with 1000kV power system.Key words: relay protection； fault resistance； transient component； nature frequency中图分类号： TM77 文献标识码：A 文章编号： 1674-3415(2010)02-0018-040 引言随着电力负荷的增长，电力系统规模越来越大，超高压长距离输电线路也在相应的增加，和中短线路不同，长距离输电线路分布电容较大，衰减时间常数较大，使得故障暂态过程持续时间较长[1-2]。
GGALVANIC DISTORTIONThe electrical conductivity of Earth materials affects two physical processes:electromagnetic induction which is utilized with magneto-tellurics(MT)(q.v.),and electrical conduction.If electromagnetic induction in media which are heterogeneous with respect to their elec-trical conductivity is considered,then both processes take place simul-taneously:Due to Faraday’s law,a variational electric field is induced in the Earth,and due to the conductivity of the subsoil an electric cur-rent flows as a consequence of the electric field.The current compo-nent normal to boundaries within the heterogeneous structure passes these boundaries continously according tos1E1¼s2E2where the subscripts1and2indicate the boundary values of conductiv-ity and electric field in regions1and2,respectively.Therefore the amplitude and the direction of the electric field are changed in the vicinity of the boundaries(Figure G1).In electromagnetic induction studies,the totality of these changes in comparison with the electric field distribution in homogeneous media is referred to as galvanic distortion. The electrical conductivity of Earth materials spans13orders of mag-nitude(e.g.,dry crystalline rocks can have conductivities of less than 10–6S mÀ1,while ores can have conductivities exceeding106S mÀ1). Therefore,MT has a potential for producing well constrained mod-els of the Earth’s electrical conductivity structure,but almost all field studies are affected by the phenomenon of galvanic distortion, and sophisticated techniques have been developed for dealing with it(Simpson and Bahr,2005).Electric field amplitude changes and static shiftA change in an electric field amplitude causes a frequency-indepen-dent offset in apparent resistivity curves so that they plot parallel to their true level,but are scaled by a real factor.Because this shift can be regarded as spatial undersampling or“aliasing,”the scaling factor or static shift factor cannot be determined directly from MT data recorded at a single site.If MT data are interpreted via one-dimensional modeling without correcting for static shift,the depth to a conductive body will be shifted by the square root of the factor by which the apparent resistivities are shifted.Static shift corrections may be classified into three broad groups: 1.Short period corrections relying on active near-surface measurementssuch as transient electromagnetic sounding(TEM)(e.g.,Meju,1996).2.Averaging(statistical)techniques.As an example,electromagneticarray profiling is an adaptation of the magnetotelluric technique that involves sampling lateral variations in the electric field con-tinuously,and spatial low pass filtering can be used to suppress sta-tic shift effects(Torres-Verdin and Bostick,1992).3.Long period corrections relying on assumed deep structure(e.g.,a resistivity drop at the mid-mantle transition zones)or long-periodmagnetic transfer functions(Schmucker,1973).An equivalence relationship exists between the magnetotelluric impedance Z and Schmucker’s C-response:C¼Zi om0;which can be determined from the magnetic fields alone,thereby providing an inductive scale length that is independent of the dis-torted electric field.Magnetic transfer functions can,for example, be derived from the magnetic daily variation.The appropriate method for correcting static shift often depends on the target depth,because there can be a continuum of distortion at all scales.As an example,in complex three-dimensional environments near-surface correction techniques may be inadequate if the conductiv-ity of the mantle is considered,because electrical heterogeneity in the deep crust creates additional galvanic distortion at a larger-scale, which is not resolved with near-surface measurements(e.g.,Simpson and Bahr,2005).Changes in the direction of electric fields and mixing of polarizationsIn some target areas of the MT method the conductivity distribution is two-dimensional(e.g.,in the case of electrical anisotropy(q.v.))and the induction process can be described by two decoupled polarizations of the electromagnetic field(e.g.,Simpson and Bahr,2005).Then,the changes in the direction of electric fields that are associated with galvanic distortion can result in mixing of these two polarizations. The recovery of the undistorted electromagnetic field is referred to as magnetotelluric tensor decomposition(e.g.,Bahr,1988,Groom and Bailey,1989).Current channeling and the“magnetic”distortionIn the case of extreme conductivity contrasts the electrical current can be channeled in such way that it is surrounded by a magneticvariational field that has,opposite to the assumptions made in the geo-magnetic deep sounding(q.v.)method,no phase lag with respect to the electric field.The occurrence of such magnetic fields in field data has been shown by Zhang et al.(1993)and Ritter and Banks(1998).An example of a magnetotelluric tensor decomposition that includes mag-netic distortion has been presented by Chave and Smith(1994).Karsten BahrBibliographyBahr,K.,1988.Interpretation of the magnetotelluric impedance tensor: regional induction and local telluric distortion.Journal of Geophy-sics,62:119–127.Chave,A.D.,and Smith,J.T.,1994.On electric and magnetic galvanic distortion tensor decompositions.Journal of Geophysical Research,99:4669–4682.Groom,R.W.,and Bailey,R.C.,1989.Decomposition of the magneto-telluric impedance tensor in the presence of local three-dimensional galvanic distortion.Journal of Geophysical Research,94: 1913–1925.Meju,M.A.,1996.Joint inversion of TEM and distorted MT sound-ings:some effective practical considerations.Geophysics,61: 56–65.Ritter,P.,and Banks,R.J.,1998.Separation of local and regional information in distorted GDS response functions by hypothetical event analysis.Geophysical Journal International,135:923–942. Schmucker,U.,1973.Regional induction studies:a review of methods and results.Physics of the Earth and Planetary Interiors,7: 365–378.Simpson,F.,and Bahr,K.,2005.Practical Magnetotellurics.Cam-bridge:Cambridge University Press.Torres-Verdin,C.,and Bostick,F.X.,1992.Principles of special sur-face electric field filtering in magnetotellurics:electromagnetic array profiling(EMAP).Geophysics,57:603–622.Zhang,P.,Pedersen,L.B.,Mareschal,M.,and Chouteau,M.,1993.Channelling contribution to tipper vectors:a magnetic equivalent to electrical distortion.Geophysical Journal International,113: 693–700.Cross-referencesAnisotropy,ElectricalGeomagnetic Deep SoundingMagnetotelluricsMantle,Electrical Conductivity,Mineralogy GAUSS’DETERMINATION OF ABSOLUTE INTENSITYThe concept of magnetic intensity was known as early as1600in De Magnete(see Gilbert,William).The relative intensity of the geomag-netic field in different locations could be measured with some preci-sion from the rate of oscillation of a dip needle—a method used by Humboldt,Alexander von(q.v.)in South America in1798.But it was not until Gauss became interested in a universal system of units that the idea of measuring absolute intensity,in terms of units of mass, length,and time,was considered.It is now difficult to imagine how revolutionary was the idea that something as subtle as magnetism could be measured in such mundane units.On18February1832,Gauss,Carl Friedrich(q.v.)wrote to the German astronomer Olbers:“I occupy myself now with the Earth’s magnetism,particularly with an absolute determination of its intensity.Friend Weber”(Wilhelm Weber,Professor of Physics at the University of Göttingen)“conducts the experiments on my instructions.As, for example,a clear concept of velocity can be given only through statements on time and space,so in my opinion,the complete determination of the intensity of the Earth’s magnetism requires to specify(1)a weight¼p,(2)a length¼r,and then the Earth’s magnetism can be expressed byffiffiffiffiffiffiffip=rp.”After minor adjustment to the units,the experiment was completed in May1832,when the horizontal intensity(H)at Göttingen was found to be1.7820mg1/2mm–1/2s–1(17820nT).The experimentThe experiment was in two parts.In the vibration experiment(Figure G2) magnet A was set oscillating in a horizontal plane by deflecting it from magnetic north.The period of oscillations was determined at different small amplitudes,and from these the period t0of infinite-simal oscillations was deduced.This gave a measure of MH,where M denotes the magnetic moment of magnet A:MH¼4p2I=t20The moment of inertia,I,of the oscillating part is difficult to deter-mine directly,so Gauss used the ingenious idea of conductingtheFigure G2The vibration experiment.Magnet A is suspended from a silk fiber F It is set swinging horizontally and the period of an oscillation is obtained by timing an integral number of swings with clock C,using telescope T to observe the scale S reflected in mirror M.The moment of inertia of the oscillating part can be changed by a known amount by hanging weights W from the rodR. 278GAUSS’DETERMINATION OF ABSOLUTE INTENSITYexperiment for I and then I þD I ,where D I is a known increment obtained by hanging weights at a known distance from the suspension.From several measures of t 0with different values of D I ,I was deter-mined by the method of least squares (another of Gauss ’s original methods).In the deflection experiment,magnet A was removed from the suspension and replaced with magnet B.The ratio M /H was measured by the deflection of magnet B from magnetic north,y ,produced by magnet A when placed in the same horizontal plane as B at distance d magnetic east (or west)of the suspension (Figure G3).This required knowledge of the magnetic intensity due to a bar magnet.Gauss deduced that the intensity at distance d on the axis of a dipole is inversely proportional to d 3,but that just one additional term is required to allow for the finite length of the magnet,giving 2M (1þk/d 2)/d 3,where k denotes a small constant.ThenM =H ¼1=2d 3ð1Àk =d 2Þtan y :The value of k was determined,again by the method of least squares,from the results of a number of measures of y at different d .From MH and M /H both M and,as required by Gauss,H could readily be deduced.Present methodsWith remarkably little modification,Gauss ’s experiment was devel-oped into the Kew magnetometer,which remained the standard means of determining absolute H until electrical methods were introduced in the 1920s.At some observatories,Kew magnetometers were still in use in the 1980s.Nowadays absolute intensity can be measured in sec-onds with a proton magnetometer and without the considerable time and experimental skill required by Gauss ’s method.Stuart R.C.MalinBibliographyGauss,C.F.,1833.Intensitas vis magneticae terrestris ad mensuram absolutam revocata.Göttingen,Germany.Malin,S.R.C.,1982.Sesquicentenary of Gauss ’s first measurement of the absolute value of magnetic intensity.Philosophical Transac-tions of the Royal Society of London ,A 306:5–8.Malin,S.R.C.,and Barraclough,D.R.,1982.150th anniversary of Gauss ’s first absolute magnetic measurement.Nature ,297:285.Cross-referencesGauss,Carl Friedrich (1777–1855)Geomagnetism,History of Gilbert,William (1544–1603)Humboldt,Alexander von (1759–1859)Instrumentation,History ofGAUSS,CARL FRIEDRICH (1777–1855)Amongst the 19th century scientists working in the field of geomag-netism,Carl Friedrich Gauss was certainly one of the most outstanding contributors,who also made very fundamental contributions to the fields of mathematics,astronomy,and geodetics.Born in April 30,1777in Braunschweig (Germany)as the son of a gardener,street butcher,and mason Johann Friderich Carl,as he was named in the certificate of baptism,already in primary school at the age of nine perplexed his teacher J.G.Büttner by his innovative way to sum up the numbers from 1to ter Gauss used to claim that he learned manipulating numbers earlier than being able to speak.In 1788,Gauss became a pupil at the Catharineum in Braunschweig,where M.C.Bartels (1769–1836)recognized his outstanding mathematical abilities and introduced Gauss to more advanced problems of mathe-matics.Gauss proved to be an exceptional pupil catching the attention of Duke Carl Wilhelm Ferdinand of Braunschweig who provided Gauss with the necessary financial support to attend the Collegium Carolinum (now the Technical University of Braunschweig)from 1792to 1795.From 1795to 1798Gauss studied at the University of Göttingen,where his number theoretical studies allowed him to prove in 1796,that the regular 17-gon can be constructed using a pair of compasses and a ruler only.In 1799,he received his doctors degree from the University of Helmstedt (close to Braunschweig;closed 1809by Napoleon)without any oral examination and in absentia .His mentor in Helmstedt was J.F.Pfaff (1765–1825).The thesis submitted was a complete proof of the fundamental theorem of algebra.His studies on number theory published in Latin language as Disquitiones arithi-meticae in 1801made Carl Friedrich Gauss immediately one of the leading mathematicians in Europe.Gauss also made further pioneering contributions to complex number theory,elliptical functions,function theory,and noneuclidian geometry.Many of his thoughts have not been published in regular books but can be read in his more than 7000letters to friends and colleagues.But Gauss was not only interested in mathematics.On January 1,1801the Italian astronomer G.Piazzi (1746–1820)for the first time detected the asteroid Ceres,but lost him again a couple of weeks later.Based on completely new numerical methods,Gauss determined the orbit of Ceres in November 1801,which allowed F.X.von Zach (1754–1832)to redetect Ceres on December 7,1801.This prediction made Gauss famous next to his mathematical findings.In 1805,Gauss got married to Johanna Osthoff (1780–1809),who gave birth to two sons,Joseph and Louis,and a daughter,Wilhelmina.In 1810,Gauss married his second wife,Minna Waldeck (1788–1815).They had three more children together,Eugen,Wilhelm,and Therese.Eugen Gauss later became the founder and first president of the First National Bank of St.Charles,Missouri.Carl Friedrich Gauss ’interest in the Earth magnetic field is evident in a letter to his friend Wilhelm Olbers (1781–1862)as early as 1803,when he told Olbers that geomagnetism is a field where still many mathematical studies can be done.He became more engaged in geo-magnetism after a meeting with A.von Humboldt (1769–1859)and W.E.Weber (1804–1891)in Berlin in 1828where von Humboldt pointed out to Gauss the large number of unsolved problems in geo-magnetism.When Weber became a professor of physics at the Univer-sity of Göttingen in 1831,one of the most productive periods intheFigure G3The deflection experiment.Suspended magnet B is deflected from magnetic north by placing magnet A east or west (magnetic)of it at a known distance d .The angle of deflection y is measured by using telescope T to observe the scale S reflected in mirror M.GAUSS,CARL FRIEDRICH (1777–1855)279field of geomagnetism started.In1832,Gauss and Weber introduced the well-known Gauss system according to which the magnetic field unit was based on the centimeter,the gram,and the second.The Mag-netic Observatory of Göttingen was finished in1833and its construc-tion became the prototype for many other observatories all over Europe.Gauss and Weber furthermore developed and improved instru-ments to measure the magnetic field,such as the unifilar and bifilar magnetometer.Inspired by A.von Humboldt,Gauss and Weber realized that mag-netic field measurements need to be done globally with standardized instruments and at agreed times.This led to the foundation of the Göttinger Magnetische Verein in1836,an organization without any for-mal structure,only devoted to organize magnetic field measurements all over the world.The results of this organization have been published in six volumes as the Resultate aus den Beobachtungen des Magnetischen Vereins.The issue of1838contains the pioneering work Allgemeine Theorie des Erdmagnetismus where Gauss introduced the concept of the spherical harmonic analysis and applied this new tool to magnetic field measurements.His general theory of geomagnetism also allowed to separate the magnetic field into its externally and its internally caused parts.As the external contributions are nowadays interpreted as current systems in the ionosphere and magnetosphere Gauss can also be named the founder of magnetospheric research.Publication of the Resultate ceased in1843.W.E.Weber together with such eminent professors of the University of Göttingen as Jacob Grimm(1785–1863)and Wilhelm Grimm(1786–1859)had formed the political group Göttingen Seven protesting against constitutional violations of King Ernst August of Hannover.As a consequence of these political activities,Weber and his colleagues were dismissed. Though Gauss tried everything to bring back Weber in his position he did not succeed and Weber finally decided to accept a chair at the University of Leipzig in1843.This finished a most fruitful and remarkable cooperation between two of the most outstanding contribu-tors to geomagnetism in the19th century.Their heritage was not only the invention of the first telegraph station in1833,but especially the network of36globally operating magnetic observatories.In his later years Gauss considered to either enter the field of bota-nics or to learn another language.He decided for the language and started to study Russian,already being in his seventies.At that time he was the only person in Göttingen speaking that language fluently. Furthermore,he was asked by the Senate of the University of Göttingen to reorganize their widow’s pension system.This work made him one of the founders of insurance mathematics.In his final years Gauss became fascinated by the newly built railway lines and supported their development using the telegraph idea invented by Weber and himself.Carl Friedrich Gauss died on February23,1855as a most respected citizen of his town Göttingen.He was a real genius who was named Princeps mathematicorum already during his life time,but was also praised for his practical abilities.Karl-Heinz GlaßmeierBibliographyBiegel,G.,and K.Reich,Carl Friedrich Gauss,Braunschweig,2005. Bühler,W.,Gauss:A Biographical study,Berlin,1981.Hall,T.,Carl Friedrich Gauss:A Biography,Cambridge,MA,1970. Lamont,J.,Astronomie und Erdmagnetismus,Stuttgart,1851. Cross-referencesHumboldt,Alexander von(1759–1859)Magnetosphere of the Earth GELLIBRAND,HENRY(1597–1636)Henry Gellibrand was the eldest son of a physician,also Henry,and was born on17November1597in the parish of St.Botolph,Aldersgate,London.In1615,he became a commoner at Trinity Col-lege,Oxford,and obtained a BA in1619and an MA in1621.Aftertaking Holy Orders he became curate at Chiddingstone,Kent,butthe lectures of Sir Henry Savile inspired him to become a full-timemathematician.He settled in Oxford,where he became friends withHenry Briggs,famed for introducing logarithms to the base10.Itwas on Briggs’recommendation that,on the death of Edmund Gunter,Gellibrand succeeded him as Gresham Professor of Astronomy in1627—a post he held until his death from a fever on16February1636.He was buried at St.Peter the Poor,Broad Street,London(now demolished).Gellibrand’s principal publications were concerned with mathe-matics(notably the completion of Briggs’Trigonometrica Britannicaafter Briggs died in1630)and navigation.But he is included herebecause he is credited with the discovery of geomagnetic secular var-iation.The events leading to this discovery are as follows(for furtherdetails see Malin and Bullard,1981).The sequence starts with an observation of magnetic declinationmade by William Borough,a merchant seaman who rose to“captaingeneral”on the Russian trade route before becoming comptroller ofthe Queen’s Navy.The magnetic observation(Borough,1581,1596)was made on16October1580at Limehouse,London,where heobserved the magnetic azimuth of the sun as it rose through sevenfixed altitudes in the morning and as it descended through the samealtitudes in the afternoon.The mean of the two azimuths for each alti-tude gives a measure of magnetic declination,D,the mean of which is11 190EÆ50rms.Despite the small scatter,the value could have beenbiased by site or compass errors.Some40years later,Edmund Gunter,distinguished mathematician,Gresham Professor of Astronomy and inventor of the slide rule,foundD to be“only6gr15m”(6 150E)“as I have sometimes found it oflate”(Gunter,1624,66).The exact date(ca.1622)and location(prob-ably Deptford)of the observation are not stated,but it alerted Gunterto the discrepancy with Borough’s measurement.To investigatefurther,Gunter“enquired after the place where Mr.Borough observed,and went to Limehouse with...a quadrant of three foot Semidiameter,and two Needles,the one above6inches,and the other10inches long ...towards the night the13of June1622,I made observation in sev-eral parts of the ground”(Gunter,1624,66).These observations,witha mean of5 560EÆ120rms,confirmed that D in1622was signifi-cantly less than had been measured by Borough in1580.But was thisan error in the earlier measure,or,unlikely as it then seemed,was Dchanging?Unfortunately Gunter died in1626,before making anyfurther measurements.When Gellibrand succeeded Gunter as Gresham Professor,allhe required to do to confirm a major scientific discovery was towait a few years and then repeat the Limehouse observation.Buthe chose instead to go to the site of Gunter’s earlier observationin Deptford,where,in June1633,Gellibrand found D to be“muchless than5 ”(Gellibrand,1635,16).He made a further measurement of D on the same site on June12,1634and“found it not much to exceed4 ”(Gellibrand,1635,7),the published data giving4 50 EÆ40rms.His observation of D at Paul’s Cray on July4,1634adds little,because it is a new site.On the strength of these observations,he announced his discovery of secular variation(Gellibrand,1635,7and 19),but the reader may decide how much of the credit should go to Gunter.Stuart R.C.Malin280GELLIBRAND,HENRY(1597–1636)BibliographyBorough,W.,1581.A Discourse of the Variation of the Compass,or Magnetical Needle.(Appendix to R.Norman The newe Attractive).London:Jhon Kyngston for Richard Ballard.Borough,W.,1596.A Discourse of the Variation of the Compass,or Magnetical Needle.(Appendix to R.Norman The newe Attractive).London:E Allde for Hugh Astley.Gellibrand,H.,1635.A Discourse Mathematical on the Variation of the Magneticall Needle.Together with its admirable Diminution lately discovered.London:William Jones.Gunter,E.,1624.The description and use of the sector,the crosse-staffe and other Instruments.First booke of the crosse-staffe.London:William Jones.Malin,S.R.C.,and Bullard,Sir Edward,1981.The direction of the Earth’s magnetic field at London,1570–1975.Philosophical Transactions of the Royal Society of London,A299:357–423. Smith,G.,Stephen,L.,and Lee,S.,1967.The Dictionary of National Biography.Oxford:University Press.Cross-referencesCompassGeomagnetic Secular VariationGeomagnetism,History ofGEOCENTRIC AXIAL DIPOLE HYPOTHESISThe time-averaged paleomagnetic fieldPaleomagnetic studies provide measurements of the direction of the ancient geomagnetic field on the geological timescale.Samples are generally collected at a number of sites,where each site is defined as a single point in time.In most cases the time relationship between the sites is not known,moreover when samples are collected from a stratigraphic sequence the time interval between the levels is also not known.In order to deal with such data,the concept of the time-averaged paleomagnetic field is used.Hospers(1954)first introduced the geocentric axial dipole hypothesis(GAD)as a means of defining this time-averaged field and as a method for the analysis of paleomag-netic results.The hypothesis states that the paleomagnetic field,when averaged over a sufficient time interval,will conform with the field expected from a geocentric axial dipole.Hospers presumed that a time interval of several thousand years would be sufficient for the purpose of averaging,but many studies now suggest that tens or hundreds of thousand years are generally required to produce a good time-average. The GAD model is a simple one(Figure G4)in which the geomag-netic and geographic axes and equators coincide.Thus at any point on the surface of the Earth,the time-averaged paleomagnetic latitude l is equal to the geographic latitude.If m is the magnetic moment of this time-averaged geocentric axial dipole and a is the radius of the Earth, the horizontal(H)and vertical(Z)components of the magnetic field at latitude l are given byH¼m0m cos l;Z¼2m0m sin l;(Eq.1)and the total field F is given byF¼ðH2þZ2Þ1=2¼m0m4p a2ð1þ3sin2lÞ1=2:(Eq.2)Since the tangent of the magnetic inclination I is Z/H,thentan I¼2tan l;(Eq.3)and by definition,the declination D is given byD¼0 :(Eq.4)The colatitude p(90 minus the latitude)can be obtained fromtan I¼2cot pð0p180 Þ:(Eq.5)The relationship given in Eq. (3) is fundamental to paleomagnetismand is a direct consequence of the GAD hypothesis.When applied toresults from different geologic periods,it enables the paleomagneticlatitude to be derived from the mean inclination.This relationshipbetween latitude and inclination is shown in Figure G5.Figure G5Variation of inclination with latitude for a geocentricdipole.GEOCENTRIC AXIAL DIPOLE HYPOTHESIS281Paleom a gnetic polesThe positio n where the time-averaged dipole axis cuts the surface of the Earth is called the paleomagnetic pole and is defined on the present latitude-longitude grid. Paleomagnetic poles make it possible to com-pare results from different observing localities, since such poles should represent the best estimate of the position of the geographic pole.These poles are the most useful parameter derived from the GAD hypothesis. If the paleomagnetic mean direction (D m , I m ) is known at some sampling locality S, with latitude and longitude (l s , f s ), the coordinates of the paleomagnetic pole P (l p , f p ) can be calculated from the following equations by reference to Figure G6.sin l p ¼ sin l s cos p þ cos l s sin p cos D m ðÀ90 l p þ90 Þ(Eq. 6)f p ¼ f s þ b ; when cos p sin l s sin l porf p ¼ f s þ 180 À b ; when cos p sin l s sin l p (Eq. 7)wheresin b ¼ sin p sin D m = cos l p : (Eq. 8)The paleocolatitude p is determined from Eq. (5). The paleomagnetic pole ( l p , f p ) calculated in this way implies that “sufficient ” time aver-aging has been carried out. What “sufficient ” time is defined as is a subject of much debate and it is always difficult to estimate the time covered by the rocks being sampled. Any instantaneous paleofield direction (representing only a single point in time) may also be con-verted to a pole position using Eqs. (7) and (8). In this case the pole is termed a virtual geomagnetic pole (VGP). A VGP can be regarded as the paleomagnetic analog of the geomagnetic poles of the present field. The paleomagnetic pole may then also be calculated by finding the average of many VGPs, corresponding to many paleodirections.Of course, given a paleomagnetic pole position with coordinates (l p , f p ), the expected mean direction of magnetization (D m , I m )at any site location (l s , f s ) may be also calculated (Figure G6). The paleocolatitude p is given bycos p ¼ sin l s sin l p þ cos l s cos l p cos ðf p À f s Þ; (Eq. 9)and the inclination I m may then be calculated from Eq. (5). The corre-sponding declination D m is given bycos D m ¼sin l p À sin l s cos pcos l s sin p; (Eq. 10)where0 D m 180 for 0 (f p – f s ) 180and180 < D m <360for 180 < (f p –f s ) < 360 .The declination is indeterminate (that is any value may be chosen)if the site and the pole position coincide. If l s ¼Æ90then D m is defined as being equal to f p , the longitude of the paleomagnetic pole.Te s ting the GAD hy p othesis Tim e scale 0– 5 MaOn the timescale 0 –5 Ma, little or no continental drift will have occurred, so it was originally thought that the observation that world-wide paleomagnetic poles for this time span plotted around the present geographic indicated support for the GAD hypothesis (Cox and Doell,1960; Irving, 1964; McElhinny, 1973). However, any set of axial mul-tipoles (g 01; g 02 ; g 03 , etc.) will also produce paleomagnetic poles that cen-ter around the geographic pole. Indeed, careful analysis of the paleomagnetic data in this time interval has enabled the determination of any second-order multipole terms in the time-averaged field (see below for more detailed discussion of these departures from the GAD hypothesis).The first important test of the GAD hypothesis for the interval 0 –5Ma was carried out by Opdyke and Henry (1969),who plotted the mean inclinations observed in deep-sea sediment cores as a function of latitude,showing that these observations conformed with the GAD hypothesis as predicted by Eq. (3) and plotted in Figure G5.Testing the axial nature of the time-averaged fieldOn the geological timescale it is observed that paleomagnetic poles for any geological period from a single continent or block are closely grouped indicating the dipole hypothesis is true at least to first-order.However,this observation by itself does not prove the axial nature of the dipole field.This can be tested through the use of paleoclimatic indicators (see McElhinny and McFadden,2000for a general discus-sion).Paleoclimatologists use a simple model based on the fact that the net solar flux reaching the surface of the Earth has a maximum at the equator and a minimum at the poles.The global temperature may thus be expected to have the same variation.The density distribu-tion of many climatic indicators (climatically sensitive sediments)at the present time shows a maximum at the equator and either a mini-mum at the poles or a high-latitude zone from which the indicator is absent (e.g.,coral reefs,evaporates,and carbonates).A less common distribution is that of glacial deposits and some deciduous trees,which have a maximum in polar and intermediate latitudes.It has been shown that the distributions of paleoclimatic indicators can be related to the present-day climatic zones that are roughly parallel with latitude.Irving (1956)first suggested that comparisons between paleomag-netic results and geological evidence of past climates could provide a test for the GAD hypothesis over geological time.The essential point regarding such a test is that both paleomagnetic and paleoclimatic data provide independent evidence of past latitudes,since the factors con-trolling climate are quite independent of the Earth ’s magnetic field.The most useful approach is to compile the paleolatitude values for a particular occurrence in the form of equal angle or equalareaFigure G6Calculation of the position P (l p ,f p )of thepaleomagnetic pole relative to the sampling site S (l s ,f s )with mean magnetic direction (D m ,I m ).282GEOCENTRIC AXIAL DIPOLE HYPOTHESIS。
1.目的：之马矢奏春创作确定与产物和过程相关的潜在的失效模式和潜在制造或装配过程失效的机理/起因, 评价潜在失效对顾客发生的后果和影响, 采用控制来降低失效发生频度或失效条件探测度的过程变量和能够防止或减少这些潜在失效发生的办法2.范围：适用于公司用于汽车零组件的所有新产物/过程或修改过的产物/过程及应用或环境发生变更的原有产物/过程的样品试制和批量生产.3．引用文件：《文件和资料控制法式》《质量记录控制法式》《产物质量先期规画控制法式》4 术语和界说：PFMEA：指Process Failure Mode and Effects Analysis （过程失效模式及后果分析）的英文简称.由负责制造/装配的工程师/小组主要采纳的一种分析技术, 用以最年夜限度地保证各种潜在的失效模式及其相关的起因/机理已获得充沛的考虑和论述.失效：在规定条件下（环境、把持、时间）, 不能完成既定功能或产物参数值和不能维持在规定的上下限之间, 以及在工作范围内招致零组件的破裂卡死等损坏现象.严重度（S）：指一给定失效模式最严重的影响后果的级别, 是单一的FMEA范围内的相对定级结果.严重度数值的降低只有通过设计更改或重新设计才华够实现.频度（O）：指某一特定的起因/机理发生的可能发生, 描述呈现的可能性的级别数具有相对意义, 但不是绝对的.探测度（D）：指在零部件离开制造工序或装配之前, 利用第二种现行过程控制方法找出失效起因/机理过程缺陷或后序发生的失效模式的可能性的评价指标；或者用第三种过程控制方法找出后序发生的失效模式的可能性的评价指标.风险优先数（RPN）：指严重度数（S）和频度数（O）及不容易探测度数（D）三项数字之乘积.顾客：一般指“最终使用者”, 但也可以是随后或下游的制造或装配工序, 维修工序或政府法规.5．职责：项目小组负责过程失效模式及后果分析（PFMEA）的制定与管理6. 工作流程和内容：7.相关记录:7.1 [PFMEA表]《潜在失效模式及后果分析 2008第四版》编制：审核：批准：日期：日期：日期：创作时间：二零二一年六月三十日编制：张朝平审核：吴玲艳批准：谈俊强创作时间：二零二一年六月三十日。
无机化学专业英语词汇1.化学原理chemic al princi ples2.气体，液体和溶液的性质thebehavi ors of gas ,liquid and solution3.理想气体定律ideal g as law4.道尔顿分压定律Dalton’s Lawof Partia l pressu res5.液体的蒸汽压v aporpressu reof liquid6.液体的凝固点freezi ng pointofliquid7.体系和环境sy stem and surrounding8.状态和状态函数states andstatefuncti ons9.化学热力学基础the basisofchemic al thermo dynam i cs10.化学平衡chemic al equili brium11.体系与状态 system and state12.热力学定理 l aw of thermo dynamics13.热化学thermochemi stry14.焓enthal py15.混乱度disord er16.熵entropy17.吉布斯自由能Gibbsf ree energy18.化学平衡chemic al equili brium19.标准平衡常数standard equilibrium consta n t20.同离子效应th e common ioneffect21.缓冲溶液buffer ed soluti on22.酸碱理论与电离平衡the theories of acids&basesand ionizationequili brium23.盐的水解the hydrol ysisofsalts24.沉淀反应the precip i tati on reactions25.溶度积solubilityproduc t26.沉淀溶解平衡的移动equili brium shiftbetween precip itati onand dissol ution27.分步沉淀stepwi se precip i tati on28.盐效应salt effect29.氧化还原反应oxidati on andreduction reacti ons30.还原剂reduci ng agent or reducer or reduct ant31.氧化剂oxidaz ing agentoroxidiz er or oxidan t32.原电池galvan i c cell33.负极，正极negati ve pole, positive pole34.阳极，阴极anode,cathod e35.燃料电池fuel cell36.电化学electr ochem i stry37.电极电势electr ode potenti al38.歧化反应dispro porti onati onreacti on39.化学动力学基础the basisofchemic al dynami cs40.化学反应速率the rate of chemical reacti on41.化学反应机理reacti on mechanism42.化学反应活化能acti vati onenergy of chemic al reacti on43.动能kineti c energy44.基元反应elemen taryreacti on45.一级反应first-order reacti on46.零级反应zero-orderreacti on47.化学理论chemic al theori es48.原子结构the atomic s structu re49.元素周期律 periodi c system ofelemen ts50.原子内部structu re in atom51.氢原子光谱th e spectrum ofatomic hydrog en52.等电子原理 i soele ctron ic principle53.电子构型electr onicstructureof atom54.价电子构型v alanc e electr on i cconfig u rati on55.主量子数princi pal quantu mnumber56.角量子数angula r quantu mnumber57.磁量子数magneti c quantu mnumber58.能级energy levels59.原子核外电子的运动状态moving statio ns of electr onsouterthe atomic nucleu s60.多电子原子结构structure inmany-electr on atoms61.周期系periodic system62.元素基本性质的周期性p eriodic propertiesof the elemen ts 63.化学键和分子，晶体结构chemic al bondsand structu resofmolecu les & crysta l s64.金属键和金属晶体metallicbond and metallic crysta l65.离子键和离子晶体ionicbondand ioniccrysta l66.晶体学基础crysta llogr aphicfounda tion67.配位化合物的基本概念b asi cconcepts of coordi natio n compounds68.异构现象isomeri sm69.化学结构异构现象chemic alstructu re isomeri sm70.电离异构体 i oniza tioni somer71.溶剂合异构体solven t isomer72.配位异构体 c oordi natio n isomer73.聚合异构体 p olymer isomer74.键连异构体 l inkag e isomer75.立体异构现象steric isomerism76.几何异构现象g eomet ri calisomeri sm77.光学异构现象optical isomerism78.手性分子chiral molecu l e79.配合物的化学键理论the chemical bond theori es of comple x es 80.价键理论valanc e bond theory 81.内轨型杂化 i nnerh ybrid i zati on82.内轨型配合物innerorbitalcoordi natio n83.外轨型杂化 outerh ybrid i zati on84.外轨型配合物outerorbitalcoordi natio n85.配位平衡coordi natio n equilibrium86.晶体场理论 crysta l fieldtheory87.强场strong field88.弱场weak field89.配合物的稳定性stabil izati on ofcomple x90.配合物的平衡常数equili bri umconsta n t of comple x91.配位化合物的应用applic ati onof coordi natio n compou n ds92.配位催化coordi natio n cataly sis93.单基配体uniden tateligand94.多基配体multid entat e ligand95.螯合物chelat e96.描述化学descri ptive chemis try97.稀有气体th e rare gases98.氢,碱金属和碱土元素hyd rogen ,alkali and alkali-earthm etals 99.卤素theh alogens100.过渡元素th e transi tionelemen ts 101.电泳 Electr ophor esis102.紫外-可见光分光光度计UV-Visibl e Spectr ophot omete r。
Multi-source information fusion based fault diagnosis of ground-source heat pump using BayesiannetworkBaoping Cai,Yonghong Liu ⇑,Qian Fan,Yunwei Zhang,Zengkai Liu,Shilin Yu,Renjie JiCollege of Mechanical and Electronic Engineering,China University of Petroleum,Qingdao,Shandong 266580,Chinah i g h l i g h t sA multi-source information fusion based fault diagnosis methodology is proposed. The diagnosis model is obtained by combining two proposed Bayesian networks. The proposed model can increase the fault diagnostic accuracy for single fault. The model can correct the wrong results for multiple-simultaneous faults.a r t i c l e i n f o Article history:Received 22July 2013Received in revised form 7September 2013Accepted 17September 2013Keywords:Multi-source information fusion Ground-source heat pump Bayesian network Fault diagnosisa b s t r a c tIn order to increase the diagnostic accuracy of ground-source heat pump (GSHP)system,especially for multiple-simultaneous faults,the paper proposes a multi-source information fusion based fault diagnosis methodology by using Bayesian network,due to the fact that it is considered to be one of the most useful models in the filed of probabilistic knowledge representation and reasoning,and can deal with the uncer-tainty problem of fault diagnosis well.The Bayesian networks based on sensor data and observed infor-mation of human being are established,respectively.Each Bayesian network consists of two layers:fault layer and fault symptom layer.The Bayesian network structure is established according to the cause and effect sequence of faults and symptoms,and the parameters are studied by using Noisy-OR and Noisy-MAX model.The entire fault diagnosis model is established by combining the two proposed Bayesian net-works.Six fault diagnosis cases of GSHP system are studied,and the results show that the fault diagnosis model using evidences from only sensor data is accurate for single fault,while it is not accurate enough for multiple-simultaneous faults.By adding the observed information as evidences,the probability of fault present for single fault of ‘‘Refrigerant overcharge’’increases to 100%from 99.69%,and the probabilities of fault present for multiple-simultaneous faults of ‘‘Non-condensable gas’’and ‘‘Expansion valve port largen’’increases to almost 100%from 61.1%and 52.3%,respectively.In addition,the observed information can correct the wrong fault diagnostic results,such as ‘‘Evaporator fouling’’.Therefore,the multi-source information fusion based fault diagnosis model using Bayesian network can increase the fault diagnostic accuracy greatly.Ó2013Elsevier Ltd.All rights reserved.1.IntroductionGround-source heat pumps (GSHP)recovering heat from ground,have been widely utilized all over the world,which result in primary energy consumption reduction up to 60%compared to conventional heating systems,are of great significance in energy saving and environment protection [1–4].Failure of the heat pump will cause reduction of energy efficiency and increment of environ-mental pollution.The relevant faults occurred in GSHP are divided into hard faults and soft faults.Generally,hard faults are easy to be detected and estimated,and soft faults are more difficult to be discovered [5].The common hard faults include (a)compressor hard shutdown;(b)valve choke completely;(c)fan stop running,and so on.And the common soft faults include:(a)refrigerant overcharge;(b)Refrigerant leakage;(c)evaporator fouling,and so on.Various fault diagnosis techniques are developed and used,to locate the soft faults exactly in heat pump systems.Using fault diagnosis techniques,the degradation performance of heat pump systems can be detected early,and the exact reasons for degradation can be diagnosed [6].Xiao et al.[7]presented a fault diagnosis strategy based on a simple regression model and a set of generic rules for centrifugal chillers.Lee et al.[8]described a scheme for on-line fault detection and diagnosis at the subsystem level in an air-handling unit using general regression neural net-works,which consisted of process estimation,residual generation,0306-2619/$-see front matter Ó2013Elsevier Ltd.All rights reserved./10.1016/j.apenergy.2013.09.043Corresponding author.Tel.:+86053286983303;fax:+86053286983300.E-mail address:liuyhupc@ (Y.Liu).and fault detection and diagnosis.Wang and Cui[9]developed an online strategy to detect,diagnose and validate sensor faults in centrifugal using principal-component analysis method.Mohanraj et al.[10,11]review the applications of artificial neural networks for refrigeration,air conditioning and heat pumps,and presented the suitability of artificial neural network to predict the perfor-mance of a direct expansion solar assisted heat pump,and the experiments were performed.Li and Braun[12]extended the decoupling-based fault detection and diagnosis method to heat pumps,and developed diagnostic features for leakage within check valves and reversing valves.Sun et al.[13]developed an online sensor fault detection and diagnosis strategy based on data fusion technology to detect faults in the building cooling load direct mea-surement.Najafiet al.[14]developed diagnostic algorithms for air handling units that can address such constraints more effectively, such as modeling limitations,measurement constraints,and the complexity of concurrent faults,by systematically employing ma-chine-learning techniques.Gang and Wang[15]developed artifi-cial neural network models for predicting the temperature of the water exiting the ground heat exchanger.A numerical simulation package of a Hybrid ground source heat pump system is adopted for training and testing the model.Bayesian network(BN)is considered to be one of the most use-ful models in thefiled of probabilistic knowledge representation and reasoning,which has been widely used in reliability evalua-tion and fault diagnosis.Cai et al.[16–18]studied the reliability of subsea blowout preventer control system,subsea blowout pre-venter operations and human factors on offshore blowouts by using Bayesian network or dynamic Bayesian ngseth and Portinale[19]and Weber et al.[20]presented a bibliograph-ical review over the last decade on the application of Bayesian network to reliability,dependability,risk analysis and mainte-nance.Recently,the application of Bayesian network on fault diagnosis has been investigated deeply.Dey and Stori[21]devel-oped and presented a process monitoring and diagnosis approach based on a Bayesian belief network for incorporating multiple process metrics from multiple sensor sources in sequential machining operations to identify the root cause of process varia-tions and provide a probabilistic confidence level of the diagnosis. Sahin et al.[22]presented a fault diagnosis system for airplane engines using Bayesian networks and distributed particle swarm optimization.Gonzalez et al.[23]developed a methodology for the real-time detection and quantification of instrument gross er-ror.Zhu et al.[24]proposed an active and dynamic method of diagnosis of crop diseases to achieve rapid and precise diagnosis of crop diseases,using Bayesian networks to represent the rela-tionships among the symptoms and crop diseases.However,there are few application of Bayesian network in the heating,ventila-tion,and air conditioning system.Zhao et al.[25]proposed a gen-eric intelligent fault detection and diagnosis strategy to simulate the actual diagnostic thinking of chiller experts,and developed a three-layer diagnostic Bayesian network to diagnose chiller faults based on the Bayesian network theory.In order to increase the diagnostic accuracy,especially for mul-tiple-simultaneous faults,this work presented a multi-source information fusion based fault diagnosis methodology for GSHP system by using Bayesian network method.The proposed Bayesian network consists of two layers:fault layer and fault symptom layer.The fault symptom layer includes not only sensor data but also observed information,which can increase the fault diagnostic accuracy greatly.The paper is structured as follows:Section2pre-sents the faults and fault symptoms of GSHP system.In Section3, the fault diagnosis methodology is developed using Bayesian net-work.In Section4,the fault diagnosis results using evidences from sensor data and observed information is researched.Section5 summarizes the paper.2.Faults and fault symptomsThe schematic diagram of a GSHP system in the heating mode is depicted in Fig.1[26–28].The system mainly consists of three ma-jor circuits:(a)the ground heat exchanger circuit,(b)the heat pump unit circuit,and(c)the indoor fan coil circuit[29–32].The ground heat exchanger circuit composes of a ground heat exchan-ger and a water pump;the heat pump unit circuit composes of a compressor,an evaporator,a condenser,an electronic expansion valve and a4-way valve;and the indoor fan coil circuit composes of several indoor fan coils and a water pump.The Coefficient of Performance(COP)of the GSHP system is3.5,and the noise can be controlled less than65decibels.As mentioned above,the soft faults of GSHP system are difficult to detect,which are diagnosed by monitoring the system status. According to references review and practical experience,eight soft faults are imposed in this work:(a)refrigerant overcharge(ReOv);(b)refrigerant leakage(ReLe);(c)evaporator fouling(EvFo);(d) condenser fouling(CoFo);(e)non-condensable gas(NcGa);(f) compressor suction or discharge valve leakage(CoVL);(g)expan-sion valve port largen(ExPL);and(h)high pressure pipe line block-age(HPLB)[33–36].Each fault has two states,which are present and absent.The status of GSHP is monitored by using temperature sensors and pressure sensors.The fault symptoms therefore include:(a) evaporating pressure(EvaPr,Pe);(b)condensing pressure(ConPr, Pc);(c)evaporating temperature(EvaTe,Te);(d)condensing temperature(ConTe,Tc);(e)compressor suction temperature (ComST,Ts);(f)compressor discharge temperature(ComDT,Td);(g)evaporator water temperature difference(EvaTD,D Te);and(h)condenser water temperature difference(ConTD,D Tc).Each fault symptom obtained from sensor data has three states,which are higher,lower and normal.The relationship between faults and symptoms obtained from sensor data are given in Table1.Taking ReLe for example,the refrigerant in the heat pump unit circuit decreases because of refrigerant leakage,making both of evaporating pressure and con-densing pressure decrease.The refrigerant discharge superheat temperature therefore increases,making the compressor suction temperature and discharge temperature increase.Due to the fact that the heat pump work in a state of ill health with insufficient refrigerant,the heat absorption capacity and heating capacity de-crease,therefore,all of the evaporating temperature,condensing temperature,evaporator water temperature difference and con-denser water temperature difference decrease.In addition,several fault symptoms can be observed directly by human being,such as(a)compressor can not stop;(b)compressor surface frost;and(c)compressor vibration.These symptoms can help to diagnose the faults of GSHP system more accurately.The relationship between faults and symptoms obtained from observed information is given in Table2.Similarly,taking ReLe for example, four observed fault symptoms including(a)too much foam (ToMuF),(b)compressor surface frost(CoSuF),(c)pungent odor (PunOd),and(d)grease stains in wiped joint(GrSWJ)can be caused by refrigerant leakage.Each fault symptom obtained from observed information has two states,which are present and absent.3.Fault diagnosis methodology3.1.Fault diagnosis based on sensor dataThe fault diagnosis model of GSHP system is established by using Bayesian network method.Specifically,each Bayesian net-2 B.Cai et al./Applied Energy114(2014)1–9work is constructed in two consecutive steps,which are defining the network structure and defining the network parameters.3.1.1.Bayesian network structureThe Bayesian network structure is established according to the cause and effect sequence of events.In this work,faults of GSHP system,such as refrigerant overcharge and evaporator fouling,are the causes;and fault symptoms,such as evaporating pressure is higher compressor suction temperature is lower,are the conse-quences.The relationship is denoted by an arc between them.According to the relationship between faults and fault symptoms obtained from sensor data given in Table 1,the Bayesian networks for fault diagnosis are established as shown in Fig.2.The proposed Bayesian network structure consists of two layers:fault layer and fault symptom layer.The fault layer consists of eight parent nodes,indicating eight potential faults concerned.The symptom layer consists of eight child nodes,indicating eight fault symptoms ob-tained from sensor data.Taking the node EvaPr for example,it isconnected to its eight parent nodes according to eight arcs,which indicates that the fault symptom EvaPr is related to all of the eight faults.3.1.2.Bayesian network parametersThe prior probabilities and conditional probabilities are re-quired to specify for Bayesian networks.The prior probability of an event is the probability of the event computed before the arrival of new evidence or information.It is obtained according to experi-ences of experts and statistical analysis of historical data.The high-er the prior probability of an event,the more likely the event is to happen.For the GSHP system,the same prior probabilities of faults are assumed,in order to emphasize the posterior probabilities gi-ven new evidences.As shown in Fig.2,the probabilities of faults are all 2%.A conditional probability is the probability that an event will occur,when another event is known to occur or to have occurred.It is also obtained according to experiences of experts and statisti-cal analysis of historical data.One of the major issues faced is the exponential growth of the number of parameters in the conditional probability tables.The specification of a complete conditional probability table for a child node m with s m states and n parentnodes requires the assessment of ðs m À1ÞQn i ¼1s i probabilities,where s i is the number of states of parent node i [37].The most common practical solution is the application of Noisy-MAX to sim-plify the conditional probability tables.The noisy gate needs to meet following three assumptions:(a)the child node and all its parents must be variables indicating the degree of presence of an anomaly;(b)each of the parent node must represent a cause that can produce the effect (the child node)in the absence of the other causes;and (c)there may be no significant synergies among thecauses [38].Therefore,only Pn 1ðs m À1Þs i probabilities arerequiredyout of a GSHP system in the heating mode.Table 1Relationship between faults and symptoms obtained from sensor data.FaultFault symptom EvaPrConPr EvaTe ConTe ComST ComDT EvaTD ConTD ReOv Higher Higher Higher N/a Lower Lower Normal Higher ReLe Lower Lower Lower Lower Higher Higher Lower Lower EvFo Lower Lower Lower Lower Lower Lower Higher Higher CoFo Lower Higher Higher Higher Higher Higher Lower Higher NcGa Lower Higher N/a Higher Higher Higher Lower Lower CoVL Lower Lower Higher Lower Higher Lower Lower Lower ExPL Higher Higher Higher N/a Lower Lower Lower Normal HPLBLowerHigherLowerLowerHigherHigherLowerLowerTable 2Relationship between faults and symptoms obtained from observed information.Fault Fault symptomReOvCompressor can not stop (CoNoS)Compressor surface frost (CoSuF)Compressor vibration (CoVib)ReLeToo much foam (ToMuF)Compressor surface frost (CoSuF)Pungent odor (PunOd)Grease stains in wiped joint (GrSWJ)NcGa Compressor discharge pressure gauge vibration (CoGaV)CoVL Compressor can not stop (CoNoS)ExPLToo much foam (ToMuF)Compressor surface frost (CoSuF)to specify the conditional probability tables,thereby simplifying knowledge acquisition greatly.Suppose for example,there are n causes X 1,X 2,...and X n of Y ,by using Noisy-MAX,the full conditional probability relationship can be written as [39,40]P ðY 6y j X Þ¼Y n i ¼1x i –0X y y 0¼0q xi i ;y 0ð1ÞP ðY ¼y j X Þ¼P ðY 60j X Þif y ¼0;P ðY 6y j X ÞÀP ðY 6y À1j X Þif y >0:ð2Þwhere X represents a certain configuration of the parents of Y ,X =x 1,...,x n ,and P (Y =0|X 1=0,...,X n =0)=1.It can be seen from Table 1that when a fault occurs,the corre-sponding fault symptom are occurs theoretically.For example,the fault ReOv causes the fault symptom EvaPr ‘‘Higher’’.However,in practice,the fault symptom is uncertain,for example,the fault ReOv causes the fault symptom EvaPr ‘‘Higher’’,‘‘Lower’’or ‘‘Nor-mal’’.The existed uncertainty problem is caused by various rea-sons,and sensor accuracy and measure uncertainty are the important causes.In the current work,one designer and two repairmen of GSHP systems were invited to determine the rela-tionship between parent nodes and child nodes for sensor data,as given in Table 3.By using the relationship and Eqs.(1)and (2),the conditional probability table can becomputed.Fig.2.Bayesian networks for fault diagnosis using sensor data.Table 3Relationship between parent nodes and child nodes for sensor data.Child nodeStateParent node (present)ReOvReLe EvFo CoFo NcGa CoVL ExPL HPLB EvaPrHigher 0.800.000.110.040.120.110.780.10Lower 0.050.950.850.680.590.820.010.89Normal 0.150.050.040.280.290.070.210.01ConPrHigher 0.750.000.080.780.990.010.840.73Lower 0.050.900.690.050.000.830.050.10Normal 0.200.100.230.170.010.160.110.17EvaTeHigher 0.650.000.200.840.010.820.900.01Lower 0.100.920.720.120.010.020.010.80Normal 0.250.080.080.040.980.160.090.19ConTeHigher 0.970.050.120.780.890.000.010.05Lower 0.000.810.870.100.000.880.020.89Normal 0.030.140.010.120.110.120.970.06ComSTHigher 0.020.690.050.860.590.850.080.84Lower 0.860.000.650.140.100.010.690.02Normal 0.120.310.300.000.310.140.230.14Fig.3.Bayesian networks for fault diagnosis using observed information.Fig.5.Flow chart for the development of fault diagnosis methodology.3.3.Multi-source information fusion based fault diagnosisIn order to increase the fault diagnostic accuracy of GSHP sys-tem,the data and information obtained from sensor and humanbeing are fused by suing Bayesian network method,and the entirefault diagnosis model shown in Fig.4is established by combiningthe two sub-model in Figs.2and3.Taking the fault‘‘Refrigerantovercharge’’for example,it can cause not only the change of eight Fig.4.The entire Bayesian networks for fault diagnosis.4.1.Fault diagnosis using evidences from only sensor dataTable 5gives three fault diagnosis cases using evidences from only sensor data,and the fault diagnosis results is shown in Fig.6.For the case No.1,when the eight sensor data are set as the evidences in the Bayesian networks shown in Fig.2,the poster-ior probabilities of all of the faults are calculated,as shown in Fig.6(a).It is can be seen that the probability of fault present for ‘‘Refrigerant overcharge’’is 99.69%,and the probabilities for other seven fault are almost 0.The diagnostic result is in accordance with the fault found in the practical operation.It indicates that the fault diagnosis model using evidences from only sensor data is accurate Table 5Three fault diagnosis cases using evidences from only sensor data.EvidenceCase No.1No.2No.3EvaPr Higher Lower Lower ConPr Higher Higher Lower EvaTe Higher Higher Lower ConTe Higher Higher Lower ComST Lower Lower Higher ComDT Lower Lower Lower EvaTD Normal Lower Lower ConTDHigherLowerLowerFig.6.Three fault diagnosis results using evidences from only sensor data (a)Case No.1,(b)Case No.2and (c)Case No.3.6 B.Cai et al./Applied Energy 114(2014)1–9Fig.7.Three fault diagnosis results using evidences from only sensor data(a)Case No.1+,(b)Case No.2+and(c)Case No.3+.increases to almost 100%from 61.1%and 52.3%,respectively.The two cases show that the observed information can increase the fault diagnostic accuracy greatly.As shown in Fig.8(c),the probability of fault present for ‘‘Evap-orator fouling’’decreases to 1.8%from 51.5%,while the probability of ‘‘Refrigerant leakage’’increase to 98.3%from 28.2%,and the probability of ‘‘Compressor suction or discharge valve leakage’’in-crease to 96.2%from 43.8%.The diagnostic result is in accordancewith the faults found in the practical operation.Therefore,the ob-served information can correct the wrong fault diagnostic results.Above all,the multi-source information fusion based fault diag-nosis model can increase the fault diagnostic accuracy greatly.According to the research above,it can be seen that the proposed Bayesian network based fault diagnosis methodology is different from other artificial intelligence method based fault diagnosis,such as artificial neural networks [10]and data fusion technology [13]based fault diagnosis.The proposed methodology can deal with the uncertainty of fault and fault symptoms well.For example,the present of ReOv can cause EvaPr higher,lower and normal with three probabilities of 80%,5%and 15%,which can be defined in the conditional probability table of Bayesian net-works.In addition,several new observed information can be added into the fault diagnosis model easily to increase the diagnosis accuracy.5.ConclusionsIn order to increase the diagnostic accuracy,especially for mul-tiple-simultaneous faults,the work proposed a multi-source infor-mation fusion based fault diagnosis methodology for GSHP system.(1)The entire fault diagnosis model of GSHP system is estab-lished by combing two proposed Bayesian networks,which are established according to the cases and effect sequence of faults and fault symptoms,including sensor data and observed information of human being.(2)The fault diagnosis model using evidences from only sensordata is accurate for single fault,for example,the probability of fault present for single fault of ‘‘Refrigerant overcharge’’is 99.69%.(3)The fault diagnosis model using evidences from only sensordata is not accurate enough for multiple-simultaneous faults,for example,the faults ‘‘Evaporator fouling’’and ‘‘Compressor suction or discharge valve leakage’’have the maximum posterior probabilities of 51.5%and 43.8%,which are not in accordance with the faults found in the practical operation.(4)The observed information can increase the fault diagnosticaccuracy greatly for single fault,for example,the probability of fault present for ‘‘Refrigerant overcharge’’increases to 100%from 99.69%,while the probabilities of other faults decreases slightly.(5)The observed information can increase the fault diagnosticaccuracy greatly as well as correct the wrong fault diagnostic results for multiple-simultaneous faults.For example,the probabilities of fault present for ‘‘Non-condensable gas’’and ‘‘Expansion valve port largen’’increases to almost 100%from 61.1%and 52.3%,respectively.(6)The cases show that the multi-source information fusionbased fault diagnosis model using Bayesian network is effec-tual for GSHP system.The work focuses on the Bayesian network based fault diagnosis methodology,and a future scope of work can be directed toward the development and validation of 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position regulator 位置第器 position type telemeter 位置式遥测计 positional servosystem 位置伺服系统 positioning 位置蝶 positioning control 定位控制 positioning element 定位部件 positive blower 增压⿎风机 positive booster 增压机 positive charge 正电荷 positive column 阳极区 positive coupling 正连接 positive electricity 阳电 positive electrode 阳极 positive electron 阳电⼦ positive feedback 正反馈 positive feedback amplifier 正反馈放⼤器 positive feeder 正馈电线 positive ion 阳离⼦ positive phase sequence reactance 正相序电抗 positive phase sequence resistance 正相序电阻 positive plate 阳极板 positive pole 阳极 positive reactance 感抗 positive self regulation 正⾃动第 positive sequence 正序 positive sequence component 正序分量 positive sequence current 正序电流 positive sequence voltage 正序电压 positive temperature coefficient 正温度系数 positive terminal 正极端⼦ positron 阳电⼦ post emergency conditions 事故后状况 post fault conditions 事故后状况 post insulator ⽀座绝缘⼦ post office box 箱式电桥 pot head cable end sleeve 电缆终端套管 potassium 钾 potential 电位 potential barrier 位垒 potential circuit 电压回路 potential coil 电压线圈 potential difference 电位差 potential distribution 电位分布 potential divider 分压器 potential drop 电势降 potential energy 位能 potential field 位场 potential gradient 位梯度 potential hill 位垒 potential hydroenergy ⽔⼒藏量 potential peak 尖头电位 potential peak periods 可能的峰值产⽣期间 potential slope 位梯度 potential to ground 对地电势 potential transformer 电压互感器 potential winding 电压绕组 potentiometer 电位计 potentiometer method 电位计法 potentiometer resistance 电位计电阻 potentiometer slider 电位计滑臂 potentiometer transducer 电位计传感器 potentiometer type rheostat 电位计式变阻器 potentiometer type voltage divider 电位计式分压器 potier's coefficient of equivalence 保梯等效系数 potier's electromotive force 保梯电动势 potier's reactance 保梯电抗 poulsen arc 浦⽿⽣电弧 powdered coal 粉煤 powdered magnet 压粉磁铁 power 功率 power amplification 功率放⼤ power amplifier 功率放⼤器 power angle 负载⾓ power angle curve 功率功⾓曲线 power brake 动⼒制动 power breeder 动⼒增殖堆 power cable 电⼒电缆 power circuit 电⼒电路 power consumer 电⼒⽤户 power consumption 电⼒消耗 power current 电⼒电流续流 power decrease 功率降低 power delivery 功率输出 power demand 电⼒需量 power detection 强信号检波 power directional relay 功率⽅向继电器 power distribution 配电 power divider 功率分配器 power drive 电⼒传动 power efficiency 出⼒效率 power electronics ⼯业电⼦学 power engineering 电⼒⼯程 power exchange 功率交换 power factor 功率因数 power factor characteristic 功率因数特性 power factor improvement 功率因数改进 power factor indicator 功率因数表 power factor measurement 功率因数测量 power factor meter 功率因数表 power flow 电⼒潮流 power frequency ⼯业频率 power frequency withstand voltage ⼯业频率耐压 power germanium rectifier ⼤功率锗整流 power grid 强信号栅极 power house auxiliary drive 电⼚⾃⽤设备驱动 power in rush 功率骤增 power input 输⼊功率 power input to machine 电机输⼊功率 power inverter 功率逆变器 power level 功率级 power limit 功率极限 power limitation 功率限制 power line 电⼒线 power line carrier channel 电⼒线载波通路 power loss 功率损耗 power measurement 功率测量 power meter ⽡特计 power operation 动⼒操作 power output 输出功率 power panel 配电盘 power plant 发电⼚ power plant consumption 发电⽤消耗量 power pool 联合电⼒系统 power pool dispatching office 联合系统等所 power protection 功率保护装置 power range 功率范围 power rating 额定功率 power reactor 动⼒反应堆 power rectifier ⼤功率整流 power regulator 电⼒第器功率第器 power relay 电⼒继电器 power source 电源 power spectrum density 功率谱密度 power stage 功率级 power supply system 供电系统 power surge 功率骤增 power switchboard 动⼒配电盘 power system 电⼒系统 power system separation 电⼒系统分离 power system stabilizer 电⼒系统稳定器 power thyristor 功率闸淋 power transfer relay 电源转换继电器 power transformer 电⼒变压器 power transistor 功率晶体管 power transmission 输电 power transmission line 输电线 power tube 功率管 power unit 供电设备 power valve 功率管 power vector 功率⽮量 power winding 输出线圈 poynting's vector 波印廷⽮量 practical electrical units 实⽤电单位 pre emphasis 预修正 pre impregnated insulation 预浸渍绝缘纸 preamplifier 前置放⼤器 precision 精密度 precision ammeter 精密电另 precision instrument 精密仪器 precision measurement 精密测量 precision potentiometer 精密电位计 precision type bridge 精密电桥 precision voltmeter 精密电压计 precooler 预冷器 prediction 预测 prediction operator 预测算⼦ prediction unit 预测装置 predictive control 预测控制 prefix notation 前缀表⽰法 preformed winding 成形绕组 preheating 预热 preheating time 预热时间 preimpregnation 预浸渍 prepayment meter 预付式电度表 preset parameter 预定参数 press 压⼒机 press button switch 按钮开关 press span 压板纸 pressboard 压板 pressboard insulation 压制板绝缘 presspahn 压板 pressure 压⼒ pressure cable 充⽓电缆 pressure cell 压⼒元件 pressure coil 电压线圈 pressure compound impulse turbine 复式压⼒级冲动式涡轮机 pressure control 压⼒控制 pressure control servovalve 压⼒第伺服阀 pressure control valve 压⼒第阀 pressure device 加压装置 pressure difference transducer 压差变换器 pressure drop 压降 pressure flow diagram 压⼒量图 pressure gage 压⼒计 pressure gain 压⼒增益 pressure gas circuit breaker ⾼压煤⽓断路器 pressure governor 压⼒第器 pressure head 压头 pressure indicator 指压器压⼒计 pressure ratio control 压⼒⽐率控制 pressure switch 压⼒操纵开关 pressure test 压⼒试验 pressure transducer 压⼒变换器压⼒传送器 pressure tunnel 压⼒隧道 pressure water reactor 压⽔堆 pressure welding 加压焊接 pressurization ⾼压密封 pressurized casing 加压外壳 prestressed concrete 预应⼒混凝⼟ preventive maintenance 预防性维修 price 价格 primary ⼀次的 primary battery 原电池 primary cell 原电池 primary circuit ⼀次电路 primary coil ⼀次线圈 primary coolant ⼀次冷却剂 primary current ⼀次电流 primary distribution main ⼀次配电⼲线 primary electron ⼀次电⼦ primary emission ⼀次发射 primary feedback 执馈 primary impedance ⼀次绕组阻抗 primary inductance 初级线圈电感 primary ionization 初级电离 primary light source ⼀次光源 primary relay ⼀次继电器 primary side ⼀次侧 primary source ⼀次光源 primary standard 标准原器原标准器 primary system ⼀次系统 primary voltage ⼀次电压 primary winding ⼀次绕组 prime cost 原价 principal axis 轴 principal characteristic 重性 principal clock 母钟 principle of conservation of energy 能量守恒原理 principle of optimality 性原理 principle of superposition 迭加原理 printed capacitor 印刷电容器 printed circuit 印刷电路 printed circuit assembly 印刷电路装配 printed circuit board 印刷电路板 printed coil 印刷线圈 printed motor 印刷电动机 printer 打印机 printing machine control 印刷机控制 prism 棱镜 private exchange 专⽤交换机 private installation 专⽤设备 private wire 专⽤线 probability 概率 probability curve 概率曲线 probability density 概率密度 probability distribution 概率分布 probability distribution function 概率分布函数 probability fucntion 概率因数 probe 探针 proceed signal 通过信号 process 过程 process automation 过程⾃动化 process control 过程控制 process control computer 过程控制计算器 process dynamics 过程动态 process steam ⽣产⽤蒸汽 process variable 过程变量 processing 加⼯ processor 处理器 producer gas 煤⽓ producibility of hydro electric power station ⽔⼒发电站发电量 product control ⽣产管理 product relay 乘积继电器 production cost ⽣产成本 production planning ⽣产计划 production process ⽣产程序 production reactor ⽣产堆 productive capacity ⽣产能⼒ program 程序 program control 程序控制 program control system 程序控制系统 program interruption 程序中断 program language 程序设计语⾔ program library 程序库 program loader 程序装⼊器 program register 程序寄存器 program sensitive fault 特定程序故障 program setting mechanism 程序设定机构 program with floating point 浮点程序 programmable logic control 可编程序的逻辑控制 programmable read only memory 可编程序的只读存储器 programmed check 程序校验 programmed control 程序控制 programmer 程序设计装置;程序设计员 programming 程序设计 programming controller 程序控制器 progression 级数 progressive motion servometer 步进伺服电机 progressive wave 前进波 projection 投射 projector 投光器投影机 proof stress 屈服强度 propagation 传播 propagation constant 传播常数 propeller generator 螺旋桨式发电机 propeller pump 螺旋浆式⽔泵 propeller water turbine 螺旋桨式汽轮机 proportional 成⽐例的 proportional action ⽐例动作 proportional amplifier ⽐例放⼤器 proportional and differential action ⽐例微分酌 proportional arm ⽐例臂 proportional band ⽐例范围 proportional control ⽐例控制 proportional controller ⽐例控制器 proportional counter tube 正⽐计数管 proportional element ⽐例元素 proportional gain ⽐例放⼤率 proportional integral action controller ⽐例积分控制器 proportional integral and differential action control ⽐例积分微分控制 proportional plus integral control ⽐例积分控制 proportional plus reset plus rate action control system ⽐例积分微分控制系统 proportional plus reset plus rate action controller ⽐例积分微分控制器 proportional puls reset puls rate action control ⽐例积分微分控制 proportional region ⽐例范围 proportional reset controller ⽐例积分控制器 proportional sensibility ⽐例灵敏度 proportionality ⽐例性 prospective current of circuit 电路的预期电流 protected machine 防护式电机 protected motor 防护式电动机 protected zone 防护带 protection 保护 protection equipment 保护装置 protection for interturn short circuits 匝间短路保护装置 protection ground 保护接地 protective 保护的 protective armature 保护七 protective automation 保护⾃动化 protective capacitor 保护电容器 protective circuit 保护电路 protective coat 保护敷层 protective covering 保护敷层 protective device 保护装置 protective earth 保护接地 protective equipment 保护装置 protective fuse 保护熔线 protective gap 保护隙 protective gear 保护装置 protective horn 消弧⾓保护放电间隙 protective net 保护 protective reactance coil 保护电抗器保护扼⼒ protective reactor 保护电抗器保护扼⼒ protective relay 保护继电器 protective resistance 保护电阻 protective resistor 保护电阻 protective spark gap 保护隙 protector 保护装置 proton 质⼦ proton microscope 质⼦显微镜 proton synchrotron 质⼦同步加速器 prototype 原器 prototype tests 原型试验 protractor 分度器 proximity effect 邻近效应 psophometer 噪声计 psophometric electromotive force 杂⾳表指⽰电动势 psophometric voltage 杂⾳表指⽰电压 pull button 牵引电钮 pull in test 牵⼊同步试验 pull in torque 牵⼊转矩 pull off insulator 拉线绝缘⼦ pull out torque 牵出转矩 pull switch 拉线开关 pull through winding 穿⼊绕组 pull up torque 最低起动转矩 pulling into synchronism 牵⼊同步 pulling out of synchronism 失步 pulsating current 脉动电流 pulsating electromotive force 脉动电动势 pulsating load 脉动负载 pulsating magnetic field 脉动磁场 pulsating magnetizing force 脉动磁化⼒ pulsating quantity 脉动量 pulsating rectified current 脉动整羚流 pulsating voltage 脉动电压 pulsation 脉动;交羚的⾓频率 pulsation coefficient 脉动率 pulsation welding 脉动焊接 pulse 脉冲 pulse action 脉冲动作 pulse amplifier 脉冲放⼤器 pulse amplitude 脉冲振幅 pulse amplitude modulation 脉冲振幅灯 pulse analyzer 脉冲分析器 pulse averaging circuit 脉冲平滑电路 pulse carrier 脉冲载波 pulse chopper 脉冲断续器 pulse circuit 脉冲电路 pulse circuits theory 脉冲电路理论 pulse code 脉冲代码 pulse code modulation 脉冲编码灯 pulse coder 脉冲编码器 pulse coding 脉冲编码 pulse coincidence 脉冲符合 pulse commutator 脉冲换向器 pulse control 脉冲控制 pulse control system 脉冲控制系统 pulse counter 脉冲计数器 pulse current 脉冲电流 pulse decay time 脉冲衰减时间 pulse delay 脉冲延迟 pulse discharge 脉冲放电 pulse discharging voltage 脉冲放电电压 pulse distortion 脉冲畸变 pulse duration 脉冲持续时间 pulse duration control 脉冲持续时间控制 pulse duration modulation 脉冲持续时间灯 pulse duration transmission system 脉冲宽度传送制 pulse edge 脉冲前沿 pulse fall time 脉冲衰减时间 pulse frequency 脉冲频率 pulse frequency method 脉冲频率法 pulse frequency modulation 脉冲档;脉冲频率灯 pulse frequency transmission system 脉冲频率传送制 pulse front 脉冲前沿 pulse function 脉冲函数 pulse generator 脉冲发⽣器 pulse height 脉冲⾼度。
fault simulation原理Fault Simulation原理简介•Fault Simulation（故障模拟）是一项重要的技术，用于测试和验证电路设计的可靠性和健壮性。
•本文将深入介绍Fault Simulation的原理，从浅入深地解释相关概念和步骤。
目录1.什么是Fault Simulation–故障模拟的定义和作用2.故障模拟的基本原理–故障模型和错误模型的区别–故障触发条件和错误检测条件3.故障模拟的流程–故障注入–故障激励–故障传播–错误检测4.故障模拟的应用领域–电路验证–电路故障定位–电路故障修复1. 什么是Fault Simulation•Fault Simulation，即故障模拟，是一项广泛应用于电路验证和故障定位的技术。
•它通过模拟电路中的故障，检测故障对电路的影响，以评估电路设计的可靠性和鲁棒性。
•故障模拟可以帮助设计人员发现潜在的故障、消除设计缺陷，以提高电路的可靠性和性能。
2. 故障模拟的基本原理故障模型和错误模型的区别•故障模型（Fault Model）是指用来描述故障类型和特征的数学模型，如单粒子击中、电路元件短路等。
•错误模型（Error Model）是指由故障模型引起的电路输出错误的模型，如错误传播路径和错误影响范围等。
•故障模拟通过将故障模型应用于电路中，得到错误模型，进而分析和评估错误对电路的影响。
故障触发条件和错误检测条件•故障触发条件是指使故障产生效应的特定输入模式或状态。
•错误检测条件是指通过检测错误输出来判断故障是否被成功触发。
•故障模拟需要基于故障触发条件和错误检测条件来设计测试用例，并模拟对电路的影响。
3. 故障模拟的流程故障注入•故障注入是指将事先定义好的故障模型注入到电路中的过程，以引起错误输出。
•故障注入可以通过改变电路中的电压、电流或者逻辑状态等方式来实现。
•注入的故障模型一般是根据实际故障情况或者测试需求预先定义好的。
故障激励•故障激励是指对电路注入故障后，对电路输入进行激励以引起错误输出。
