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)q?<

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,”P(pán)POPP’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第一節(jié)活塞式空壓機(jī)的工作原理第二節(jié)活塞式空壓機(jī)的結(jié)構(gòu)和自動(dòng)控制第三節(jié)活塞式空壓機(jī)的管理復(fù)習(xí)思考題單擊此處輸入你的副標(biāo)題，文字是您思想的提煉，為了最終演示發(fā)布的良好效果，請(qǐng)盡量言簡(jiǎn)意賅的闡述觀點(diǎn)。第六章活塞式空氣壓縮機(jī)

piston-aircompressor壓縮空氣在船舶上的應(yīng)用：

1.主機(jī)的啟動(dòng)、換向；

2.輔機(jī)的啟動(dòng)；

3.為氣動(dòng)裝置提供氣源；

4.為氣動(dòng)工具提供氣源；

5.吹洗零部件和濾器。

排氣量:單位時(shí)間內(nèi)所排送的相當(dāng)?shù)谝患?jí)吸氣狀態(tài)的空氣體積。單位：m3/s、m3/min、m3/h第六章活塞式空氣壓縮機(jī)

piston-aircompressor空壓機(jī)分類(lèi)：按排氣壓力分：低壓0.2～1.0MPa；中壓1～10MPa；高壓10～100MPa。按排氣量分：微型<1m3/min；小型1～10m3/min；中型10～100m3/min；大型>100m3/min。第六章活塞式空氣壓縮機(jī)

piston-aircompressor第一節(jié)活塞式空壓機(jī)的工作原理容積式壓縮機(jī)按結(jié)構(gòu)分為兩大類(lèi)：往復(fù)式與旋轉(zhuǎn)式兩級(jí)活塞式壓縮機(jī)單級(jí)活塞壓縮機(jī)活塞式壓縮機(jī)膜片式壓縮機(jī)旋轉(zhuǎn)葉片式壓縮機(jī)最長(zhǎng)的使用壽命-

----低轉(zhuǎn)速（1460RPM），動(dòng)件少（軸承與滑片），潤(rùn)滑油在機(jī)件間形成保護(hù)膜，防止磨損及泄漏，使空壓機(jī)能夠安靜有效運(yùn)作；平時(shí)有按規(guī)定做例行保養(yǎng)的JAGUAR滑片式空壓機(jī)，至今使用十萬(wàn)小時(shí)以上，依然完好如初，按十萬(wàn)小時(shí)相當(dāng)于每日以十小時(shí)運(yùn)作計(jì)算，可長(zhǎng)達(dá)33年之久。因此，將滑片式空壓機(jī)比喻為一部終身機(jī)器實(shí)不為過(guò)?；?葉)片式空壓機(jī)可以365天連續(xù)運(yùn)轉(zhuǎn)并保證60000小時(shí)以上安全運(yùn)轉(zhuǎn)的空氣壓縮機(jī)1.進(jìn)氣2.開(kāi)始?jí)嚎s3.壓縮中4.排氣1.轉(zhuǎn)子及機(jī)殼間成為壓縮空間，當(dāng)轉(zhuǎn)子開(kāi)始轉(zhuǎn)動(dòng)時(shí)，空氣由機(jī)體進(jìn)氣端進(jìn)入。2.轉(zhuǎn)子轉(zhuǎn)動(dòng)使被吸入的空氣轉(zhuǎn)至機(jī)殼與轉(zhuǎn)子間氣密范圍，同時(shí)停止進(jìn)氣。3.轉(zhuǎn)子不斷轉(zhuǎn)動(dòng)，氣密范圍變小，空氣被壓縮。4.被壓縮的空氣壓力升高達(dá)到額定的壓力后由排氣端排出進(jìn)入油氣分離器內(nèi)。4.被壓縮的空氣壓力升高達(dá)到額定的壓力后由排氣端排出進(jìn)入油氣分離器內(nèi)。1.進(jìn)氣2.開(kāi)始?jí)嚎s3.壓縮中4.排氣1.凸凹轉(zhuǎn)子及機(jī)殼間成為壓縮空間，當(dāng)轉(zhuǎn)子開(kāi)始轉(zhuǎn)動(dòng)時(shí)，空氣由機(jī)體進(jìn)氣端進(jìn)入。2.轉(zhuǎn)子轉(zhuǎn)動(dòng)使被吸入的空氣轉(zhuǎn)至機(jī)殼與轉(zhuǎn)子間氣密范圍，同時(shí)停止進(jìn)氣。3.轉(zhuǎn)子不斷轉(zhuǎn)動(dòng)，氣密范圍變小，空氣被壓縮。螺桿式氣體壓縮機(jī)是世界上最先進(jìn)、緊湊型、堅(jiān)實(shí)、運(yùn)行平穩(wěn)，噪音低，是值得信賴(lài)的氣體壓縮機(jī)。螺桿式壓縮機(jī)氣路系統(tǒng)：

A

進(jìn)氣過(guò)濾器

B

空氣進(jìn)氣閥

C

壓縮機(jī)主機(jī)

D

單向閥

E

空氣/油分離器

F

最小壓力閥

G

后冷卻器

H

帶自動(dòng)疏水器的水分離器油路系統(tǒng)：

J

油箱

K

恒溫旁通閥

L

油冷卻器

M

油過(guò)濾器

N

回油閥

O

斷油閥冷凍系統(tǒng)：

P

冷凍壓縮機(jī)

Q

冷凝器

R

熱交換器

S

旁通系統(tǒng)

T

空氣出口過(guò)濾器螺桿式壓縮機(jī)渦旋式壓縮機(jī)

渦旋式壓縮機(jī)是20世紀(jì)90年代末期開(kāi)發(fā)并問(wèn)世的高科技?jí)嚎s機(jī)，由于結(jié)構(gòu)簡(jiǎn)單、零件少、效率高、可靠性好，尤其是其低噪聲、長(zhǎng)壽命等諸方面大大優(yōu)于其它型式的壓縮機(jī)，已經(jīng)得到壓縮機(jī)行業(yè)的關(guān)注和公認(rèn)。被譽(yù)為“環(huán)保型壓縮機(jī)”。由于渦旋式壓縮機(jī)的獨(dú)特設(shè)計(jì)，使其成為當(dāng)今世界最節(jié)能壓縮機(jī)。渦旋式壓縮機(jī)主要運(yùn)動(dòng)件渦卷付，只有磨合沒(méi)有磨損，因而壽命更長(zhǎng)，被譽(yù)為免維修壓縮機(jī)。

由于渦旋式壓縮機(jī)運(yùn)行平穩(wěn)、振動(dòng)小、工作環(huán)境安靜，又被譽(yù)為“超靜壓縮機(jī)”。

渦旋式壓縮機(jī)零部件少，只有四個(gè)運(yùn)動(dòng)部件,壓縮機(jī)工作腔由相運(yùn)動(dòng)渦卷付形成多個(gè)相互封閉的鐮形工作腔，當(dāng)動(dòng)渦卷作平動(dòng)運(yùn)動(dòng)時(shí)，使鐮形工作腔由大變小而達(dá)到壓縮和排出壓縮空氣的目的。活塞式空氣壓縮機(jī)的外形第一節(jié)活塞式空壓機(jī)的工作原理一、理論工作循環(huán)（單級(jí)壓縮）工作循環(huán)：4—1—2—3