数据挖掘概念与技术(jiaweiHan授课全英文ppt)-arfid-hpdm

DataMining:

ConceptsandTechniques

—Chapter11—

—AdditionalTheme:RFIDDataWarehousingandMiningandHigh-PerformanceComputing—JiaweiHanandMichelineKamberDepartmentofComputerScienceUniversityofIllinoisatUrbana-Champaign/~hanj©2006JiaweiHanandMichelineKamber.Allrightsreserved.Acknowledgements:HectorGonzalezandShengnanCong9/6/20231DataMining:ConceptsandTechniques9/6/20232DataMining:ConceptsandTechniquesOutlineIntroductiontoRFIDTechnologyMotivation:WhyRFID-Warehousing?RFID-WarehouseArchitecturePerformanceStudyLinkingRFIDDataAnalysiswithHPCConclusions9/6/20233DataMining:ConceptsandTechniquesWhatisRFID?RadioFrequencyIdentification(RFID)Technologythatallowsasensor(reader)toread,fromadistance,andwithoutlineofsight,auniqueelectronicproductcode(EPC)associatedwithatagTagReader9/6/20234DataMining:ConceptsandTechniquesRFIDSystemSource:9/6/20235DataMining:ConceptsandTechniquesApplicationsSupplyChainManagement:real-timeinventorytrackingRetail:ActiveshelvesmonitorproductavailabilityAccesscontrol:tollcollection,creditcards,buildingaccessAirlineluggagemanagement:(Britishairways)Implementedtoreducelost/misplacedluggage(20millionbagsayear)Medical:ImplantpatientswithatagthatcontainstheirmedicalhistoryPetidentification:ImplantRFIDtagwithpetownerinformation()9/6/20236DataMining:ConceptsandTechniquesOutlineIntroductiontoRFIDTechnologyMotivation:WhyRFID-Warehousing?RFID-WarehouseArchitecturePerformanceStudyLinkingRFIDDataAnalysiswithHPCConclusions9/6/20237DataMining:ConceptsandTechniquesRFIDWarehouseArchitecture9/6/20238DataMining:ConceptsandTechniquesChallengesofRFIDDataSetsDatageneratedbyRFIDsystemsisenormousduetoredundancyandlowlevelofabstractionWalmartisexpectedtogenerate7terabytesofRFIDdataperdaySolutionRequirementsHighlycompactsummaryofthedataOLAPoperationsonmulti-dimensionalviewofthedataSummaryshouldpreservethepathstructureofRFIDdataItshouldbepossibletoefficientlydrilldowntoindividualtagswhenaninterestingpatternisdiscovered9/6/20239DataMining:ConceptsandTechniquesWhyRFID-Warehousing?(1)LosslesscompressionSignificantlyreducethesizeoftheRFIDdatasetbyredundancyremovalandgroupingobjectsthatmoveandstaytogetherDatacleaning:reasoningbasedonmorecompleteinfoMulti-reading,miss-reading,error-reading,bulkymovement,…Multi-dimensionalsummary:

product,location,time,…Storemanager:CheckitemmovementsfromthebackroomtodifferentshelvesinhisstoreRegionmanager:Collapseintra-storemovementsandlookatdistributioncenters,warehouses,andstores9/6/202310DataMining:ConceptsandTechniquesWhyRFID-Warehousing?(2)QueryProcessingSupportforOLAP:roll-up,drill-down,slice,anddicePathquery:NewtoRFID-Warehouses,aboutthestructureofpathsWhatproductsthatgothroughqualitycontrolhaveshorterpaths?Whatlocationsarecommontothepathsofasetofdefectiveauto-parts?IdentifycontainersataportthathavedeviatedfromtheirhistoricpathsDataminingFindtrends,outliers,frequent,sequential,flowpatterns,…9/6/202311DataMining:ConceptsandTechniquesExample:ASupplyChainStoreAretailerwith3,000stores,selling10,000itemsadayperstoreEachitemmoves10timesonaveragebeforebeingsoldMovementrecordedas(EPC,location,second)Datavolume:300milliontuplesperday(afterredundancyremoval)OLAPQueryAvgtimeforoutwearitemstomovefromwarehousetocheckoutcounterinMarch2006?Costlytoanswerifscanning1billiontuplesforMarch9/6/202312DataMining:ConceptsandTechniquesOutlineIntroductiontoRFIDTechnologyMotivation:WhyRFID-Warehousing?RFID-WarehouseArchitecturePerformanceStudyLinkingRFIDDataAnalysiswithHPCConclusions9/6/202313DataMining:ConceptsandTechniquesCleaningofRFIDDataRecordsRawData(EPC,location,time)Duplicaterecordsduetomultiplereadingsofaproductatthesamelocation(r1,l1,t1)(r1,l1,t2)...(r1,l1,t10)CleansedData:Minimalinformationtostore,rawdatawillbethenremoved(EPC,Location,time_in,time_out)(r1,l1,t1,t10)Warehousingcanhelpfill-upmissingrecordsandcorrectwrongly-registeredinformation9/6/202314DataMining:ConceptsandTechniquesKeyCompressionIdeas(I)BulkyobjectmovementsObjectsoftenmoveandstaytogetherthroughthesupplychainIf1000packsofsodastaytogetheratthedistributioncenter,registerasinglerecord(GID,distributioncenter,time_in,time_out)GIDisageneralizedidentifierthatrepresentsthe1000packsthatstayedtogetheratthedistributioncenterFactoryDist.Center1Dist.Center2…10pallets(1000cases)store1store2…20cases(1000packs)shelf1shelf2…10packs(12sodas)9/6/202315DataMining:ConceptsandTechniquesKeyCompressionIdeas(II)DatageneralizationAnalysisusuallytakesplaceatamuchhigherlevelofabstractionthantheonepresentinrawRFIDdataAggregateobjectmovementsintofewerrecordsIfinterestedintimeatthedaylevel,mergerecordsattheminutelevelintorecordsatthehourlevelMergeand/orcollapseofpathsegmentsUninterestingpathsegmentscanbeignoredormergedMultipleitemmovementswithinthesamestoremaybeuninterestingtoaregionalmanagerandthuscanbemerged

9/6/202316DataMining:ConceptsandTechniquesPath-IndependentGeneralizationClothingOuterwearShoesShirtJacket…SKUlevelTypelevelCategorylevelShirt1Shirtn…EPClevelCleansedRFIDDatabaseLevelInterestingLevel9/6/202317DataMining:ConceptsandTechniquesPathGeneralizationTransportationdist.centertruckbackroomshelfcheckoutbackroomshelfcheckoutdist.centertruckStoreStoreView:TransportationView:9/6/202318DataMining:ConceptsandTechniquesWhyNotUsingTraditionalDataCube?FactTable:(EPC,location,time_in,time_out)Aggregate:Ameasureatasinglelocatione.g.,whatistheaveragetimethatmilkstaysintherefrigeratorinIllinoisstores?Whatismissing?Measurescomputedonitemsthattravelthroughaseriesoflocationse.g.,whatistheaveragetimethatmilkstaysattherefrigeratorinChampaignwhencomingfromfarmA,andWarehouseB?Traditionalcubesmissthepathstructureofthedata9/6/202319DataMining:ConceptsandTechniquesRFID-CubeArchitecture9/6/202320DataMining:ConceptsandTechniquesRFID-CuboidArchitecture(II)StayTable:(GIDs,location,time_in,time_out:measures)RecordsinformationonitemsthatstaytogetheratagivenlocationIfusingrecordtransitions:difficulttoanswerqueries,lotsofintersectionsneededMapTable:(GID,<GID1,..,GIDn>)Linkstogetherstagesthatbelongtothesamepath.Providesadditional:compressionandqueryprocessingefficiencyHighlevelGIDpointstolowerlevelGIDsIfsavingcompleteEPCLists:highcostsofIOtoretrievelonglists,costlyqueryprocessingInformationTable:(EPClist,attribute1,...,attributen)Recordspath-independentattributesoftheitems,e.g.,color,manufacturer,price9/6/202321DataMining:ConceptsandTechniquesRFID-CuboidExampleepcloct_int_outr1l1t1t10r1l2t20t30r2l1t1t10r2l3t20t30r3l1t110r3l4t15t20epcsloct_int_outr1,r2,r3l1t1t10gidsgidgidsg1g1.1,g1.2g1.1r1,r2g1.2r3CleansedRFIDDatabaseStayTableMapTablegidgidsr1,r2l2t20t30g1g1.1r3l4t15t20g1.29/6/202322DataMining:ConceptsandTechniquesBenefitsoftheStayTable(I)l1l2lnlln+1ln+2ln+m……TransitionGroupingRetrievealltransitionswithdestination=lRetrievealltransitionswithorigin=lIntersectresultsandcomputeaveragetimeIOCost:n+mretrievalsPrefixTreeRetrievenrecordsStayGroupingRetrievestayrecordwithlocation=lIOCost:1Query:Whatistheaveragetimethatitemsstayatlocationl?9/6/202323DataMining:ConceptsandTechniquesBenefitsoftheStayTable(II)(r1,l1,t1,t2)(r1,l2,t3,t4)…(r2,l1,t1,t2)(r2,l2,t3,t4)…(rk,l1,t1,t2)(rk,l2,t3,t4)Query:Howmanyboxesofmilktraveledthroughthelocationsl1,l7,l13?Strategy:Retrieveitemsetsforlocationsl1,l7,l13IntersectitemsetsIOCost:OneIOperiteminlocationsl1orl7orl13Observation:Verycostly,weretrieverecordsattheindividualitemlevelStrategy:Retrievethegidsforl1,l7,l13IntersectthegidsIOCost:OneIOperGIDinlocationsl1,l7,andl13Observation:RetrieverecordsatthegrouplevelandthusgreatlyreduceIOcosts(g1,l1,t1,t2)(g2,l2,t3,t4)…WithCleansedDatabaseWithStayTable9/6/202324DataMining:ConceptsandTechniquesBenefitsoftheMapTablel1l2l3l4l5l6l7l8l9l10#EPCs#GIDsnnn1363n10+n{r1,..,ri}{ri+1,..,rj}{rj+1,..,rk}{rk+1,..,rl}{rl+1,..,rm}{rm+1,..,rn}9/6/202325DataMining:ConceptsandTechniquesPath-DependentNamingofGIDsl1l2l3l4l5l60.00.10.0.00.1.00.1.1AssigntoeachGIDauniqueidentifierthatencodesthepathtraversedbytheitemsthatitpointstoPath-dependentname:Makesiteasytodetectiflocationsformapath9/6/202326DataMining:ConceptsandTechniquesRFID-CuboidConstructionAlgorithmBuildaprefixtreeforthepathsinthecleanseddatabaseForeachnode,recordaseparatemeasureforeachgroupofitemsthatsharethesameleafandinformationrecordAssignGIDstoeachnode:GID=parentGID+uniqueidEachnodegeneratesastayrecordforeachdistinctmeasureIfmultiplenodessharethesamelocation,time,andmeasure,generateasinglerecordwithmultipleGIDs9/6/202327DataMining:ConceptsandTechniquesRFID-CubeConstructionl1l2l3l4l5l60.0t1,t10:30.1t1,t8:30.0.0t20,t30:30.1.0t20,t30:30.1.1t10,t20:2t40,t60:3t35,t50:1l3l5t40,t60:2{r1,r2,r3}{r5,r6}{r7}{r8,r9}0.0l1t1t1030.0.0l3t20t303GIDsloct_int_outcountl5t40t603l5t40t605StayTable0.1l2t1t83PathTree0.0.00.1.0l3t20t306l6t35t5010.1.1l4t10t2029/6/202328DataMining:ConceptsandTechniquesRFID-CubePropertiesTheRFID-cuboidcanbeconstructedonasinglescanofthecleansedRFIDdatabaseTheRFID-cuboidprovideslosslesscompressionatitslevelofabstractionThesizeoftheRFID-cuboidissmallerthanthecleanseddataInourexperimentsweget80%losslesscompressionatthelevelofabstractionoftherawdata9/6/202329DataMining:ConceptsandTechniquesQueryProcessingTraditionalOLAPoperationsRollup,drilldown,slice,anddiceCanbeimplementedefficientlywithtraditionaloptimizationtechniques,e.g.,whatistheaveragetimespentbymilkattheshelfPathselection(Newoperation)Computeanaggregatemeasureonthetagsthattravelthroughasetoflocationsandthatmatchaselectioncriteriaonpathindependentdimensions

stay.location='shelf',duct='milk'(staygidinfo)qÃ<

cinfo,(

c1stage1,...,

ckstagek)>9/6/202330DataMining:ConceptsandTechniques9/6/202331DataMining:ConceptsandTechniquesQueryProcessing(II)Query:Whatistheaveragetimespentfroml3tol5?GIDsforl3<0.0.0>,<0.1.0>GIDsforl5<>,<>Prefixpairs:p1:(<0.0.0>,<>)p2:(<0.1.0>,<>)Retrievestayrecordsforeachpair(includingintermediatesteps)andcomputemeasureSavings:NoEPClistintersection,rememberthateachEPClistmaycontainmillionsofdifferenttags,andretrievingthemisasignificantIOcost9/6/202332DataMining:ConceptsandTechniquesFromRFID-CuboidstoRFID-WarehouseMaterializethelowestRFID-cuboidattheminimumlevelofabstractioninterestedtoauserMaterializefrequentlyrequestedRFID-cuboidsMaterializationisdonefromthesmallestmaterializedRFID-Cuboidthatisatalowerlevelofabstraction9/6/202333DataMining:ConceptsandTechniquesOutlineIntroductiontoRFIDTechnologyMotivation:WhyRFID-Warehousing?RFID-WarehouseArchitecturePerformanceStudyLinkingRFIDDataAnalysiswithHPCConclusions9/6/202334DataMining:ConceptsandTechniquesRFID-CubeCompression(I)Compressionvs.CleanseddatasizeP=1000,B=(500,150,40,8,1),k=5Losslesscompression,cuboidisatthesamelevelofabstractionascleansedRFIDdatabaseCompressionvs.DataBulkinessP=1000,N=1,000,000,k=5MapgivessignificantbenefitsforbulkydataFordatawhereitemsmoveindividuallywearebetteroffusingtaglists9/6/202335DataMining:ConceptsandTechniquesRFID-CubeCompression(II)Compressionvs.AbstractionLevelP=1000,B=(500,150,40,8,1),k=5,N=1,000,000ThemapprovidessignificantsavingsoverusingtaglistsAtveryhighlevelsofabstractionthestaytableisverysmall,mostofthespaceisusedinrecordingRFIDtags9/6/202336DataMining:ConceptsandTechniquesRFID-CubeConstructionTimeConstructionTimeP=1000,B=(500,150,40,8,1),k=5,N=1,000,000Savingsbyconstructingfromlowerlevelcuboid50%to80%9/6/202337DataMining:ConceptsandTechniquesQueryProcessingTimevs.DBSizeP=1000,B=(500,150,40,8,1),k=5Speedupduetostaytable1orderofmagnitudeSpeedupduetostaytableandmaptable2ordersofmagnitudeTimevs.BulkinessP=1000,k=5SpeedupismostsignificantforbulkypathsFornon-bulkypathsperformanceisnotworsethanusingthecleantable9/6/202338DataMining:ConceptsandTechniquesDiscussionOurRFIDcubemodelworkswellforbulkyobjectmovementsButtherearemanyapplicationswherethisassumptionisnottrueandothermodelsareneededWehaveonlyfocusedonwarehousingRFIDdata,avarietyofotherproblemsremainopen:PathclassificationandclusteringWorkflowanalysisTrendanalysisSophisticatedRFIDdatacleaning9/6/202339DataMining:ConceptsandTechniquesOutlineIntroductiontoRFIDTechnologyMotivation:WhyRFID-Warehousing?RFID-WarehouseArchitecturePerformanceStudyLinkingRFIDDataAnalysiswithHPCConclusions9/6/202340DataMining:ConceptsandTechniquesLinkingRFIDDataAnalysiswithHPCHighperformancecomputingwillplayanimportantroleinRFIDdatawarehousinganddataanalysisMostofdatacleaningprocesscanbedoneinparallelanddistributedmannerStayandmaptablesconstructioncanbeconstructedinparallelParallelcomputationandconsolidationofmulti-layerandmulti-pathdatacubesQueryandminingcanbeprocessedinparallel9/6/202341DataMining:ConceptsandTechniquesParallelRFIDDataMining:PromisingParallelcomputinghasbeensuccessfullyappliedtorathersophisticateddataminingalgorithmsParallelizingfrequentpatternmining(basedonFPgrowth)ShengnanCong,JiaweiHan,JayHoeflinger,andDavidPadua,“ASampling-basedFrameworkforParallelDataMining,”PPOPP’05(ACMSIGPLANSymp.onPrinciples&PracticeofParallelProgramming)Parallelizingsequential-patternminingalgorithm(basedonPrefixSpan)ShengnanCong,JiaweiHan,andDavidPadua,“ParallelMiningofClosedSequentialPatterns”,KDD'05ParallelFRIDdataanalysisishighlypromising9/6/202342DataMining:ConceptsandTechniquesMiningFrequentPatternsBreadth-firstsearchvs.depth-firstsearch

Depth-firstminingalgorithmisprovedtobemoreefficientDepth-firstminingalgorithmismoreconvenienttobeparallelizednullABACADAEBCBDBECDCEDEABCDEABCABDABEACDACEADEBCDBCEBDECDEABCDABCEABDEACDEBCDEABCDEnullABACADAEBCBDBECDCEDEABCDEABCABDABEACDACEADEBCDBCEBDECDEABCDABCEABDEACDEBCDEABCDE9/6/202343DataMining:ConceptsandTechniquesParallelFrequent-PatternMiningTargetplatform─distributedmemorysystemFrameworkforparallelizationStep1:EachprocessorscanslocalportionofthedatasetandaccumulatethenumbersofoccurrenceforeachitemsReductiontoobtaintheglobalnumbersofoccurrenceStep2:PartitionthefrequentitemsandassignasubsettoeachprocessorEachprocessormakesprojectionsfortheassigneditemsStep3:Eachprocessorminesthelocalprojectionsindependently9/6/202344DataMining:ConceptsandTechniquesParallelFrequent-PatternMining(2)LoadbalancingproblemSomeprojectionminingtimeistoolargerelativetotheoverallminingtimeSolution:

ThelargeprojectionsmustbepartitionedChallenge:Howtoidentifythelargeprojections?7.66%mushroom12.4%connect14.7%pumsb47.6%pumsb_star42.1%T30I0.2D1K4.15%T40I10D100KT50I5D500KDataset3.07%Maximal/Overall21.4%C10N0.1T8S8I813.8%C50N10T8S20I2.514.2%C100N5T2.5S10I1.2510.1%C200N10T2.5S10I1.2511.6%C100N20T2.5S10I1.25DatasetMaximal/Overall15.3%C100S50N105.02%C100S100N54.53%C200S25N925.9%gazelleDatasetMaximal/Overall(a)datasetsforfrequent-itemsetmining(b)datasetsforsequential-patternmining(c)datasetsforclosed-sequential-patternmining9/6/202345DataMining:ConceptsandTechniquesHowtoIdentifytheLargeProjections?Toidentifythelargeprojections,weneedanestimationoftherelativeminingtimeoftheprojectionsStaticestimationStudythecorrelationwiththedatasetparametersNumberofitems,numberofrecords,widthofrecords,…StudythecorrelationwiththecharacteristicsoftheprojectionDepth,bushiness,treesize,numberofleaves,fan-out/in,…Result─Norulefoundwiththeaboveparametersfortheprojectionminingtime9/6/202346DataMining:ConceptsandTechniquesDynamicEstimationRuntimesamplingUsetherelativeminingtimeofasampletoestimatetherelativeminingtimeofthewholedataset.Accuracyvs.overheadRandomsampling:randomselectasubsetofrecords.Notaccuratewithsmallsamplesize.e.g.Dataset—pumsb1%randomsampling

Becomesaccuratewhensamplesize>30%,butsamplingoverheadisover50%then9/6/202347DataMining:ConceptsandTechniquesSelectiveSamplingSelectivesampling:foreachrecord,someitemsareremovedInfrequent-itemsetmining:DiscardtheinfrequentitemsDiscardafractiontofthemostfrequentitems1a,c,d,f,m2b,c,f,m3b,f4b,c5a,f,mf:4b:3c:3m:3a:2d:1Supportthreshold=2,t=20%dataset1a,c,m2b,c,m3b4b,c5a,mselectivesample9/6/202348DataMining:ConceptsandTechniquesAccuracyofSelectiveSampling9/6/202349DataMining:ConceptsandTechniquesOverheadofSelectiveSampling(a)datasetsforfrequent-itemsetmining(b)datasetsforsequential-patternmining(c)datasetsforclosed-sequential-patternmining9/6/202350DataMining:ConceptsandTechniquesExperimentalSetupsTwoLinuxclustersusingupto64processorsClusterA–1GHzPentiumIIIprocessor,1GBmemoryClusterB–1.3GHzIntelItanium2processor,2GBmemoryImplementwithC++usingMPIDatasetgeneratorfromIBMDatasets9/6/202351DataMining:ConceptsandTechniquesExperimentalSetups9/6/202352DataMining:ConceptsandTechniquesSpeedupswithOne-LevelTaskPartitioningParallelfrequent-itemsetmining9/6/202353DataMining:ConceptsandTechniquesEffectivenessofSelectiveSamplingMulti-leveltaskpartitioning9/6/202354DataMining:ConceptsandTechniquesSpeedupswithOne-LevelTaskPartitioning(SequentialPatterns)Parallelsequential-patternmining9/6/202355DataMining:ConceptsandTechniquesSpeedupswithOne-LevelTaskPartitioning(ClosedSequentialPattern)Parallelclosed-sequential-patternmining9/6/202356DataMining:ConceptsandTechniquesEffectivenessofSelectiveSamplingOne-leveltaskpartitioningwith64processorsThespeedupsareimprovedbymorethan50%onaverage.9/6/202357DataMining:ConceptsandTechniquesConclusionsAnewRFIDwarehousemodelallowsefficientandflexibleanalysisofRFIDdatainmultidimensionalspacepreservesthestructureofthedatacompressesdatabyexploitingbulkymovements,concepthierarchies,andpathcollapsingHigh-performancecomputingwillbenefitRFIDdatawarehousinganddataminingtremendouslyEfficientandhighlyparallelalgorithmscanbedevelopedforRFIDdataanalysis9/6/202358DataMining:ConceptsandTechniques9/6/202359DataMining:ConceptsandTechniques第一节活塞式空压机的工作原理第二节活塞式空压机的结构和自动控制第三节活塞式空压机的管理复习思考题单击此处输入你的副标题，文字是您思想的提炼，为了最终演示发布的良好效果，请尽量言简意赅的阐述观点。第六章活塞式空气压缩机

piston-aircompressor压缩空气在船舶上的应用：

1.主机的启动、换向；

2.辅机的启动；

3.为气动装置提供气源；

4.为气动工具提供气源；

5.吹洗零部件和滤器。

排气量:单位时间内所排送的相当第一级吸气状态的空气体积。单位：m3/s、m3/min、m3/h第六章活塞式空气压缩机

piston-aircompressor空压机分类：按排气压力分：低压0.2～1.0MPa；中压1～10MPa；高压10～100MPa。按排气量分：微型<1m3/min；小型1～10m3/min；中型10～100m3/min；大型>100m3/min。第六章活塞式空气压缩机

piston-aircompressor第一节活塞式空压机的工作原理容积式压缩机按结构分为两大类：往复式与旋转式两级活塞式压缩机单级活塞压缩机活塞式压缩机膜片式压缩机旋转叶片式压缩机最长的使用寿命-

----低转速（1460RPM），动件少（轴承与滑片），润滑油在机件间形成保护膜，防止磨损及泄漏，使空压机能够安静有效运作；平时有按规定做例行保养的JAGUAR滑片式空压机，至今使用十万小时以上，依然完好如初，按十万小时相当于每日以十小时运作计算，可长达33年之久。因此，将滑片式空压机比喻为一部终身机器实不为过。滑(叶)片式空压机可以365天连续运转并保证60000小时以上安全运转的空气压缩机1.进气2.开始压缩3.压缩中4.排气1.转子及机壳间成为压缩空间，当转子开始转动时，空气由机体进气端进入。2.转子转动使被吸入的空气转至机壳与转子间气密范围，同时停止进气。3.转子不断转动，气密范围变小，空气被压缩。4.被压缩的空气压力升高达到额定的压力后由排气端排出进入油气分离器内。4.被压缩的空气压力升高达到额定的压力后由排气端排出进入油气分离器内。1.进气2.开始压缩3.压缩中4.排气1.凸凹转子及机壳间成为压缩空间，当转子开始转动时，空气由机体进气端进入。2.转子转动使被吸入的空气转至机壳与转子间气密范围，同时停止进气。3.转子不断转动，气密范围变小，空气被压缩。螺杆式气体压缩机是世界上最先进、紧凑型、坚实、运行平稳，噪音低，是值得信赖的气体压缩机。螺杆式压缩机气路系统：

A

进气过滤器

B

空气进气阀

C

压缩机主机

D

单向阀

E

空气/油分离器

F

最小压力阀

G

后冷却器

H

带自动疏水器的水分离器油路系统：

J

油箱

K

恒温旁通阀

L

油冷却器

M

油过滤器

N

回油阀

O

断油阀冷冻系统：

P

冷冻压缩机

Q

冷凝器

R

热交换器

S

旁通系统

T

空气出口过滤器螺杆式压缩机涡旋式压缩机

涡旋式压缩机是20世纪90年代末期开发并问世的高科技压缩机，由于结构简单、零件少、效率高、可靠性好，尤其是其低噪声、长寿命等诸方面大大优于其它型式的压缩机，已经得到压缩机行业的关注和公认。被誉为“环保型压缩机”。由于涡旋式压缩机的独特设计，使其成为当今世界最节能压缩机。涡旋式压缩机主要运动件涡卷付，只有磨合没有磨损，因而寿命更长，被誉为免维修压缩机。

由于涡旋式压缩机运行平稳、振动小、工作环境安静，又被誉为“超静压缩机”。

涡旋式压缩机零部件少，只有四个运动部件,压缩机工作腔由相运动涡卷付形成多个相互封闭的镰形工作腔，当动涡卷作平动运动时，使镰形工作腔由大变小而达到压缩和排出压缩空气的目的。活塞式空气压缩机的外形第一节活塞式空压机的工作原理一、理论工作循环（单级压缩）工作循环：4—1—2—3