Industrial Applications of Machine Learning
Pedro Larrañaga David Atienza
Javier Diaz-Rozo
Alberto Ogbechie
Carlos Puerto-Santana
Concha Bielza
CRC Press
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2.2.1.1VisualizationandSummaryofUnivariateData
2.2.2.1ParameterPointEstimation.........
2.2.2.3HypothesisTesting..............
2.3.3SpectralClustering....................
2.4.1ModelPerformanceEvaluation..............
2.4.1.1PerformanceEvaluationMeasures.......
2.4.1.2HonestPerformanceEstimationMethods..
2.4.2FeatureSubsetSelection.................
2.4.3 k-NearestNeighbors...................
2.4.8LogisticRegression....................
2.4.9BayesianNetworkClassifiers..............
2.4.9.1DiscreteBayesianNetworkClassifiers....
2.4.9.2ContinuousBayesianNetworkClassifiers..
2.5.1FundamentalsofBayesianNetworks..........
2.5.2InferenceinBayesianNetworks.............
2.5.2.1TypesofInference...............
2.5.2.2ExactInference................
2.5.2.3ApproximateInference.............
2.5.3LearningBayesianNetworksfromData........
2.5.3.1LearningBayesianNetworkParameters...
2.5.3.2LearningBayesianNetworkStructures.....
2.6ModelingDynamicScenarioswithBayesianNetworks....
2.6.1DataStreams.......................
2.6.2 Dynamic,TemporalandContinuousTimeBayesian Networks.........................
2.6.3HiddenMarkovModels.................
2.6.3.1 EvaluationoftheLikelihoodofanObservation Sequence....................
2.6.3.2Decoding....................
2.6.3.3HiddenMarkovModelTraining........
3ApplicationsofMachineLearninginIndustrialSectors
3.1.2Gas............................
3.2BasicMaterialsSector......................
3.2.1Chemicals.........................
3.2.2BasicResources......................
3.4.1Retail...........................
3.4.3Tourism..........................
3.5HealthcareSector........................
3.5.1Cancer...........................
3.5.2Neuroscience.......................
3.5.3Cardiovascular......................
3.6.2FoodandBeverages...................
3.6.3PersonalandHouseholdGoods.............
3.7TelecommunicationsSector...................
3.7.1SoftwareforNetworkAnalysis..............
3.7.2DataTransmission.....................
3.8UtilitiesSector..........................
3.8.1UtilitiesGeneration...................
3.9.1Customer-FocusedApplications..............
3.9.2Operations-FocusedApplications............
3.9.3TradingandPortfolioManagementApplications...
3.9.4RegulatoryComplianceandSupervisionApplications
3.10InformationTechnologySector.................
3.10.1Hardwareandsemi-conductors.............
3.10.2Software..........................
3.10.3DataCenterManagement................
3.10.4Cybersecurity.......................
4Component-LevelCaseStudy:RemainingUsefulLifeof
4.2.1Data-DrivenTechniques.................
4.2.2PRONOSTIATestbed..................
4.3FeatureExtractionfromVibrationSignals..........
4.4HiddenMarkovModel-BasedRULEstimation........
4.4.1HiddenMarkovModelConstruction...........
4.5ResultsandDiscussion.....................
4.5.1RULResults.......................
4.5.2InterpretationoftheDegradationModel........
4.6ConclusionsandFutureResearch................
4.6.1Conclusions.........................
4.6.2FutureResearch......................
5Machine-LevelCaseStudy:FingerprintofIndustrialMotors
5.1Introduction...........................
5.2PerformanceofIndustrialMotorsasaFingerprint......
5.2.1ImprovingReliabilityModelswithFingerprints....
5.2.2IndustrialInternetConsortiumTestbed.........
5.2.3TestbedDatasetDescription..............
5.3ClusteringAlgorithmsforFingerprintDevelopment.....
5.3.1AgglomerativeHierarchicalClustering.........
5.3.2 K-meansClustering...................
5.3.3SpectralClustering....................
5.3.4AffinityPropagation...................
5.3.5GaussianMixtureModelClustering...........
5.3.6ImplementationDetails.................
5.4ResultsandDiscussion.....................
5.5ConclusionsandFutureResearch...............
5.5.1Conclusions........................
5.5.2FutureResearch.....................
6Production-LevelCaseStudy:AutomatedVisualInspection ofaLaserProcess
6.2.1ImageAcquisition.....................
6.2.2ResponseTimeRequirement..............
6.3AnomalyDetection-BasedAVISystem............
6.3.1AnomalyDetectionAlgorithmsinImageProcessing. 216
6.3.1.1ProbabilisticAnomalyDetection........
6.3.1.2Distance-BasedAnomalyDetection......
6.3.1.3Reconstruction-BasedAnomalyDetection..
6.3.1.4Domain-BasedAnomalyDetection......
6.3.2ProposedMethodology..................
6.3.2.1FeatureExtraction...............
6.3.2.2DynamicBayesianNetworksImplementation
6.3.2.3PerformanceAssessment............
6.4ResultsandDiscussion......................
6.4.1PerformanceoftheAVISystem..............
6.4.2InterpretationoftheNormalityModel.........
6.4.2.1
6.4.2.2 RelationshipsintheDynamicBayesian
6.5.1Conclusions........................
6.5.2FutureResearch......................
7Distribution-LevelCaseStudy:ForecastingofAirFreight Delays
7.2AirFreightProcess........................
7.2.1DataPreprocessing....................
7.2.1.1SimplificationofPlanned/ActualTimes...
7.2.1.2TransportLegReordering............
7.2.1.3AirportSimplification.............
7.2.1.4 NormalizingtheLengthofEachBusiness Process......................
7.3SupervisedClassificationAlgorithmsforForecastingDelays.
7.3.1 k-NearestNeighbors...................
7.3.2ClassificationTrees....................
7.3.3RuleInduction......................
7.3.4ArtificialNeuralNetworks................
7.3.5SupportVectorMachines................
7.3.6LogisticRegression.....................
7.3.7BayesianNetworkClassifiers...............
7.3.8Metaclassifiers......................
7.3.9ImplementationDetailsofClassificationAlgorithms.
7.4ResultsandDiscussion.....................
7.4.1ComparedClassifiers....................
7.4.2QuantitativeComparisonofClassifiers.........
7.4.2.1MultipleHypothesisTesting.........
7.4.2.2OnlineClassificationofBusinessProcesses.
7.4.3QualitativeComparisonofClassifiers...........
7.4.3.1C4.5........................
7.4.3.2RIPPER....................
7.4.3.3BayesianNetworkClassifiers.........
7.4.4FeatureSubsetSelection.................
7.5ConclusionsandFutureResearch...............
7.5.1Conclusions........................
7.5.2FutureResearch......................
Preface
ThefourthIndustrialRevolution,knownasIndustry4.0orIndustrialInternetofThings,isnowinfullswingandhavingamajorimpactonindustrial companiesofdifferentsectors,suchasautomation,automotivebranch,chemistry,construction,consumerservices,energy,finance,healthcare,information technologiesandtelecommunications.Theamountofindustrialdatageneratedbymachinecontrollers,sensors,manufacturingsystems,etc.isgrowing exponentially,andintelligentsystemsabletotransformthishugequantityof dataintoknowledge,asrepresentedbymathematicalandstatisticalmodels, aremorethannecessary.Machinelearningisapartofartificialintelligence thatallowstobuildthosemodels.Machinelearningcomprisesseveralmethods enablingthistransformationinsuchawaythattheresultingsoftwaresystems canprovideactionableinsightstowardsoptimaldecisions.Thesedecisionsare presentindifferentindustrialsectorsinproblemssuchasdiagnosis,predictive maintenance,conditioningmonitoring,assetshealthmanagement,etc.
Thisbookaimstoshowhowmachinelearningmethodscanbeappliedto addressreal-worldindustrialproblemsenablingthefourthindustrialrevolution andprovidingtherequiredknowledgeandtoolstoempowerreaderstobuild theirownsolutionsfoundeduponasolidtheoreticalandpracticalgroundwork. Thebookisorganizedintosevenchapters.Chapter1introducesthereaderto thefourthindustrialrevolutiondiscussingthecurrentsituation,opportunities, trends,issuesandchallenges.Chapter2focusesonmachinelearningfundamentalsandcoversthemostcommonlyusedtechniquesandalgorithmsinan understandablewayforanyreaderwithaminimummathematicaltraining. Clustering,supervisedclassification,Bayesiannetworksandthemodelingof dynamicscenariosarethediscussedtopics.Chapter3summarizessuccessfulapplicationsofmachinelearninginseveralindustrialsectorsorganized accordingtotheIndustryClassificationBenchmarkofFTSERussell.The nextfourchapterspresentfourdetailedcasestudiesofourownorganized hierarchicallyintofourlevelsofabstractioninindustrysmartization:atthe componentlevel,themachinelevel,theproductionlevel,andfinallyatthe distributionlevel.Chapter4discussestheuseofhiddenMarkovmodelsfor estimatingdegradationinarealballbearingremainingusefullifeproblem. ThedatasetwasborrowedfromtheIEEEPrognosisandHealthManagement 2012DataChallenge.Chapter5dealswithmachinetoolaxisservomotors. TheanalyzeddatasethasbeenpresentedbyAingura-IIoTandXilinx,Inc. asatestbedintheIndustrialInternetConsortium.Thebehaviorofseveral clusteringalgorithms,suchasagglomerativehierarchicalclustering, k-means,
spectralclustering,affinitypropagationandGaussianmixturemodel-based clustering,iscomparedinordertofindservomotortypefingerprints.Chapter6 showcasestheapplicationofdynamicBayesiannetworkstobuildanautomated visualinspectionsystemcapableofanalyzingimagesfromalasersurfaceheat treatmentprocess.Thedatasetwasgatheredduringarealexperimentcarried outbyIkerguneA.I.E.,theresearchanddevelopmentdepartmentofEtxe-Tar S.A.,aSpanishmanufacturingcompany.Chapter7illustrateshowmachine learningcanbeusedinthedistributionindustry.Therealdatawererecorded bytheCargoiQgroup,andcontainsdifferentshipmentspossiblycomposedof severaltransportlinesthatneedtobesynchronized.Somesupervisedclassificationmodels–k-nearestneighbors,classificationtrees,ruleinduction,artificial neuralnetworks,supportvectormachine,logisticregression,Bayesiannetwork classifiers,andmetaclassifiers–havebeenappliedtoaddressthisproblem.
Thebook’sdedicatedwebsiteat http://cig.fi.upm.es/book/ia-of-ml/ makesthefourdatasetsaccessible.Asabookofthisscopewillinevitably containsmallerrors,thewebsitealsohasaformforlettingusknowofany errorsthereadersmayfind.
Thebookprimarilytargetsprofessionals,researchersandpostgraduate studentsofbothindustrialengineeringandmachinelearningwhoareinterested inthestateoftheart,opportunities,challengesandtrendsofmachinelearning inthefourthindustrialrevolutionandareeagertoapplythelatesttechniques andalgorithmstoreal-worldproblems.Thebook’ssecondarytargetissenior managers,governmentagenciesandmembersofscientificsocietiesinterested inknowinghowthefourthindustrialrevolutionwillinfluencebusinesses,jobs orpeople’slivesandwhatmachinelearningisandhowitcanhelpaccomplish keydemandsofanewemergingworld.
Wehavebeenveryfortunatetoreceivehelpandencouragementfrom manycolleaguesandfriendswhenworkingonthisbook.Ourlabmatesatthe ComputationalIntelligentGroup(speciallyMarioMichiels)attheUniversidad PolitécnicadeMadrid,andattheAinguraIIoTandIkerguneA.I.E.,both partofEtxe-TarGroup,havebeenabletocreateaveryexcitingscientific atmospherethatweappreciateverymuch.TheconstantenthusiasmofPatxi SamaniegoatIkerguneA.I.E.duringthewholeprocesshasmeantfreshairin somedifficultsituations.Enrichingdiscussionsonthenatureofindustrialdata withJoséJuanGabilondoatEtxe-TarS.A.andDanIsaacsatXilinx,Inc.have helpedustounderstandthefourthindustrialrevolutionandmachinelearning synergies.Thisworkhasbeenpartiallysupportedbyfundingagenciessuchas theSpanishMinistryofEconomyandCompetitivenessthroughtheTIN201679684-Pproject,theSpanishMinistryofEducation,CultureandSportthrough theFPU16/00921grant,andtheRegionalGovernmentofMadridthrough theS2013/ICE-2845-CASI-CAM-CMproject,andbytheprivateFundación BBVAgranttoScientificResearchTeamsinBigData2016.
Preface xiii
PedroLarrañaga, UniversidadPolitécnicadeMadrid DavidAtienza, UniversidadPolitécnicadeMadrid JavierDiaz-Rozo, AinguraIIoT and UniversidadPolitécnicadeMadrid AlbertoOgbechie, UniversidadPolitécnicadeMadrid CarlosPuerto-Santana, UniversidadPolitécnicadeMadrid ConchaBielza, UniversidadPolitécnicadeMadrid Madrid,Spain September2018
TheFourthIndustrialRevolution
1.1Introduction
Nowadays,globaleconomiesareundergoingatechnologyshiftwithallits positiveandnegativeconnotations.Aswehavelearnedfromhistory,technologychangesenrichsocietyintermsofeducation,cohesionandemployment. However,themovementsthathavehappenedinrecenthistoryhavetaken timetobuildstructurescapableofsettingoffthedesiredleapinindustrial development.
Technologyshifts,shownin Figure1.1,arecommonlycalledindustrial revolutionsbecausetheyarecloselyrelatedtoproductivityandhavecaused disruptivechangeinmanufacturingprocessessincethe18th century.Asaresult, specificfieldsoftechnologywereimproved.Thefirstindustrialrevolution usedwaterandsteampowertomechanizeproduction.Duringthesecond industrialrevolution,waterandsteampowerwerereplacedbyelectricity, whichboostedproductivityevenfurther.Inthethirdindustrialrevolution, electronicsystemsand informationtechnologies (IT)wereusedtoincrease factoryautomation1
Today’stechnologyshiftiscalledthe fourthindustrialrevolution (4IR). Itisablurrymixtureofthedigitalandphysicalworlds,leveragingemerging digitaltechnologiesthatareabletogatherandanalyzedataacrossproduction machines,linesandsites.Itmergesthethirdindustrialrevolution’sIT,suchas computerintegratedmanufacturing(Bennett,1985),machinelearning(Samuel, 1959),theInternet(Kleinrock,1961)andmanyothertechnologies,with operationaltechnologies (OT)tocreatethedisruptivetechnologiesthatarethe backboneofthe4IR.AtechnicalreportpublishedbyPricewaterhouseCoopers (2017)listedthetoptentechnologiesasbeing:
1. Advancedmaterials withimprovedfunctionality,mechanicaland chemicalproperties,e.g.,nanomaterials.
2. Cloudtechnology capableofdeliveringcomputationalcapabilities overtheInternetwithouttheneedforlocalandexpensivemachines.
1 Thefourthindustrialrevolution:whatitmeansandhowtorespond. https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-itmeans-and-how-to-respond/
FIGURE1.1
Industrialtechnologyshifts.
3. Autonomousvehicles thatareabletonavigatewithlittleorno humanintervention,e.g.,drones.
4. Syntheticbiology thatusesengineeringprinciplestodevelop biologicalsystems,alsocalledbiotechnology.
5. Virtual(VR)oraugmentedreality(AR) generatedbyacomputertosimulateanoverlayoverthephysicalworldorawhole environment.
6. Artificialintelligence thatusesalgorithmstoperformspecific tasksemulatinghumanintelligence,e.g.,machinelearning.
7. Robotics thatusesrobotstoautomate,augmentorassisthuman activitiesaccordingtoasetofinstructionsorautonomously.
8. Blockchain thatusessoftwarealgorithmsanddistributedcomputingtorecordandconfirmtransactionsinanelectronicledger.
9. Additivemanufacturing thatbuildsfunctionalornon-functional three-dimensionalobjectsbyprintinglayeruponlayerofmaterials.
10. InternetofThings (IoT)thatnetworksdifferentobjectsembeddedwithacquisition,preprocessing,processingandcommunication capabilitiesovertheInternettoenablesmartapplications.
Allthesetechnologiescanbeusedacrosstheentire4IRlandscape:biological, digitalandphysicalworlds.However,thisbookfocusesexclusivelyonthe manufacturingindustry,wherethedigitalandphysicalworldstaketheshape ofITandadvancedmanufacturingsystemsavailableacrossdifferentindustrial sectors.Thanksmainlytonewknowledgeextractedfromdataanalytics,this combinationofthedigitalandphysicalworldshasthepotentialtoboost theproductivity,efficiencyandflexibilityofproductionsystems,increasing industrialcompetitiveness.
Betweenboththedigitalandphysicalworlds,datacouldbeaddressedasthe rawmaterialtobeconvertedintousefulknowledgetorealizetheexpectedadded valuefromthe4IR.Asrawmaterial,datahavetobeextracted,transported, stored,transformedanddeliveredtotheenduserasinformationwithadded
FIGURE1.2
Givingaddedvaluetodatafromrawtoactionableinsightsduringthe4IR.
valuedefinedasan actionableinsight (Figure1.2).Eachdatalifecyclestep isdescribedbelow:
• Extraction:Connecteddevicesgenerate2.5quintillionbytesofdataevery day2.Intheindustrialsector,dataaregeneratedbymachines:machinecontrol system,sensorsandactuators.Therefore,ideallytheonlyrequirementneeded toextractdataisahandshakeconnectionwiththedevices.However,data acquisitionisbynomeansstraightforwardbecausedataofguaranteedquality havetobecollectedatarequiredsamplingratefromdifferentdesynchronized domainsanddatasources.Forthisreason,dataacquisitionsystemsneed specialcharacteristics,suchasadvancedcommunicationtechnologies,filtering strategiesorsensorfusion,tobeabletoefficientlycaptureandsenddata inadeterministicmanner.Therefore,4IR-enabledtechnologies,suchas theIoT,areabletoprovidehigher-levelpreprocessingandconnectivity capabilitiesthatincreasedataacquisitionsystemefficiency.Forexample, wirelesssensornetworks(WSN)haveprocessing,sensingandpeer-to-peer communicationcapabilitieswheredatacanbesharedbetweennodeswithout theneedforareader.Inthiscase,thedatacouldbeacquiredindirectlyusing senseperception(Akyildizetal.,2002)fornoisefiltering.Additionally,Li etal.(2013)explainhowtheawarenessofWSNcouldbeusedtoguarantee determinismduringextraction.
• Transportation:Extracteddatahavetobemovedfromtheacquisition systemtothenextstageasefficientlyaspossible.Communicationprotocolsplayanimportantroleinenablingfast,traceable,flexibleandsecure communication.The4IRispushingfornewprotocols,suchasOPC-UA3 , RTIDDS-Secure4 orMQTT5,thatcanmeettheserequirements,enabling interoperabilitybetweendifferentdevices,real-timecommunicationanda seamlessflow.
• Storage:If2.5quintillionbytesofdataaregeneratedeveryday,thereis aneedforanappropriatestorageandmanagementsystem,providingfor efficientqueriestosupportthetransformationofdataintousableinformation.
2 https://www.ibm.com/blogs/insights-on-business/consumer-products/2-5quintillion-bytes-of-data-created-every-day-how-does-cpg-retail-manage-it/ 3 OPC-UA. https://opcfoundation.org/about/opc-technologies/opc-ua/ 4 RTIDDS-Secure. https://www.rti.com/products/secure 5 MQTT. http://mqtt.org/
Dataproductioniseverincreasing,requiringahighperformance,scalable andusablestoragesystem.Therefore,4IRhasdevelopedtheconceptofbig datawithlargerandmorecomplexdatasets.Commonlyuseddatastorage technologiesareunsuitableforbigdata.Therefore,Hadoop-basedsolutions6 , targetingthedistributedandhighlyscalablestorageoflargedatasets,such asCloudera,Hortonworks,andMapReduce,havebeendeveloped(Strohbach etal.,2016).
Inthiscase,storagecould,dependingontheneedsofthetransformationstep, belongtermorinstantaneous.Long-termstorageiswhendataanalyticsare appliedtodatabasesstoringdataontimeperiodsandtheresultsarenot timesensitive.Forexample,Kezunovicetal.(2017)describetheusageofbig datatopredicttheimpactofweatheronpowersystems,wherelargedatasets areneededtocorrectlycorrelatetheeffectsandincreasetheprediction capabilitiesofthealgorithm.Ontheotherhand,instantaneousstoragerefers totime-sensitiveinformation.Inthiscase,in-memorydatabasesareusedas ahigh-performancetemporalbufferwitharelativelysmallstoragesize.Such databasesareusuallyusedanddestroyed.
• Transformation:Thisstepisrelatedtothetransformationofdatainto actionableinsights.Machinelearningisoneofthekeytechniquesable togeneratedata-drivenpredictivemodelsthatcanbeusedfordecision making.Othertechniquesfordatatransformationarevisualanalytics.This bookfocusesprimarilyontheapplicationofmachinelearningtoindustrial applications,andthefollowingchapterssetouttheentireindustrialdatabasedfactorysmartizationprocess.Ifthetransformationsteprequiresrealtimeaccomplishment,computationalpowerisalsoneeded.Technologiessuch as field-programmablegatearray (FPGA)ortheirintegrationinto systems-on-chips(SoCs)arethecuttingedgesolutionsprovidingrobustness, lowenergyconsumption,accelerationandflexibility.SoCmanufacturers, suchasXilinx,Inc.7,arepushingforwardtowardstransformationplatforms suchasZynq® Ultrascale+™ MPSoC,wheretheirprogrammablelogicis largeenoughtoprovideaccelerationtocommonlyusedmachinelearning algorithmswithouttheneedofcomplexdevices.
• Delivery:Whentheoutputactionableinsighthastobedeliveredtotheend user.Theinsightcouldbedeliveredtothemachineoperator,plantmanager ormaintenanceengineerusingahuman-machineinterfaceordirectlytothe machineasafeedbackinsidethecontrolloop.
Theabovedatalifecycleisthe4IRbackboneformergingdigitaland physicalworlds.Thisdatalifecyclehasbeenadoptedaroundtheworld,albeit accordingtoslightlydifferentapproaches,whicharebrieflydescribedinthe followingsections.
6 ApacheHadoop. http://hadoop.apache.org/
7 https://www.xilinx.com/
1.1.1Industrie4.0
Industrie4.0 (alsocalled Industry4.0)conceptwasdefinedbyKagermann etal.(2013)asaninitiativetosecurethefutureoftheGermanmanufacturing industry.Itisabroaddefinitionthattakesintoaccounteightdifferentkey areas:
• Standardizationandreferencearchitecture. Thisisthemostactive area.TheIndustry4.0platformunderstandsthatthebestpossiblewayto enablecollaborativepartnershipsbetweencompaniesisbysharingdataand information.Sharingrequirescommonstandardsandareferencearchitecture toprovideforcommunicationbetweenpartnersandfacilitateimplementation.
• Managingcomplexsystems. Thisareafocusesonthedevelopmentof technologydesignedtomanageincreasinglycomplexproductsandmanufacturingsystems.Next-generationindustrialsystemswillbehardertomanage becauseofnovelfeaturesliketheirinterconnectivityandadaptivebehavior.
• Acomprehensivebroadbandinfrastructureforindustry. Thedevelopmentofnewgenerationsofcommunicationnetworksisimportanttobe abletoreliablysharehigh-qualitydatabetweendifferentcompanies.Data andinformationsharinghasscalabilityissuesthatneedtobesolvedandare directlyassociatedwithfactorysize.
• Safetyandsecurity. Thisisanimportantareaofactivityanddevelopment becausedataandinformationsharinghastobereliableenoughtoensure thattheproductsandproductionfacilitiesarenotadangertoeitherpeople ortheenvironment.Additionally,dataandinformationhastobeprotected againstmisuseandunauthorizedusage.Thereisaneedfornewtechnologies thatarecapableofmanaginglargeamountsofcriticaldataandinformation.
• Workorganizationanddesign. Asthefinalgoalofthisapproachistoset upinterconnectedsmartfactoriessharingdataandinformationtoimprove theproductivityofmanufacturingsystems,futurejobsneedtobeadapted totheworkflowrequirements.Forexample,repetitiveorlow-skillstaskswill bereplacedbybetter,addedvalueactivitiesthatenhanceemployeepersonal development.
• Trainingandcontinuingprofessionaldevelopment. Becauseofthe abovechangesinemployeeskillsrequirements,trainingstrategiesneedtobe reformedtoprovidethetoolsthatemployeesneedtodotheirjobinthenew workingenvironmentcreatedbytheindustrialrevolution.
• Regulatoryframework. Thenewcollaborativepartnershipslaunchedby theIndustry4.0approacharebasedondataandinformationsharingabout whichlegislationhasnotyetbeendeveloped.Aclearframeworkhasto bedesignedtohelpwiththedefinitionofdataandinformationownership boundaries,where,dependingonthedeploymentscenario,acleardistinction shouldbemadebetweenpersonal,corporate,productandprocessdata.
• Resourceefficiency. Theindustrialsectoristheworld’slargestenergy consumer,asittakesagreatdealofenergytotransformrawmaterialsinto products.Additionally,factoryinterconnection,andalltheresultingdata management,sometimesrequirestheuseofadvancedtechnologyequipment withhigherenergyrequirements.Therefore,thereisaneedtostudythe trade-offbetweenanyadditionalresourcesrequiredandpotentialenergy savingsinordertoimproveenergyusage.
Todevelopthesekeyareas,conceptslike cyber-physicalsystems (CPS) definedbyGill(2006)havebeenintroducedtosupporttheexploitationof theIoTintothemanufacturingenvironment.Therefore,Kagermannetal. (2013)definesCPSas smartmachines,storagesystemsandproduction facilitiesthatareabletoexchangeinformation,triggeractionsandcontrolin anunattendedmanner.CPShavebeenreportedtoplayanumberofdifferent roles.Themostimportant,however,isthattheyconstitutethenexusbetween thedigitalandphysicalworlds.
Therefore,substitutingtheabovedatalifecycleintoKagermannetal.’s definition,aCPSshouldbecapableofextraction,transportation,storage, transformationanddelivery.Tobeabletoenactthislifecycle,aCPSwill havetobeendowedwithartificialintelligencetobehavewithoutsupervision thanksto self-learning capabilities.Machinelearningisthespecificartificial intelligenceenablingtechnologyfor self-learning capabilities,especiallyin thetransformationstage.
ThedefinitionofIndustrie4.0doesnotincludeanyexplicitreferences toartificialintelligence.However,oneoftheauthorsofthedefinitionofthe termIndustrie4.0isProf.WolfgangWahlster,CEOoftheGermanResearch CenterforArtificialIntelligence(DFKIGmbH).Wahlsterconsidersartificial intelligenceasthemaindriverofsmartfactoriessupportedbyCPS.
Although,Industrie4.0isaGermaninitiativedesignedtoboosttheGerman manufacturingindustry,thebroadconcepthasbeenrapidlyadoptedbyalmost allEuropeancountries.Adoptionhastakenplaceatmanylevels,rangingfrom localgovernment,intheshapeofpolicies,tocompanies.
1.1.2IndustrialInternetofThings
Thefirststepstowardsthe IndustrialInternetofThings (IIoT)were describedin2012withintheframeworkoftheUnitedStatesPresident’s CouncilofAdvisorsonScienceandTechnology8.Inthisscenario,someof thecross-cuttingtechnologiesselectedforadvancedmanufacturingwereas follows:advancedsensing,informationtechnologies,digitalmanufacturingand visualization,terminologythatissimilartothedatalifecycledescribedabove. ByMarch2012,theUnitedStatesSteeringCommitteeforFoundationsfor
8 ReporttothePresidentonCapturingDomesticCompetitiveAdvantageinAdvanced Manufacturing. https://energy.gov/eere/downloads/report-president-capturingdomestic-competitive-advantage-advanced-manufacturing/
InnovationinCPS,ledbyVanderbiltUniversityandBoeing,hadsubmitted areportaboutthestrategicopportunitiesforCPSinthe21st century(Sztipanovitsetal.,2012).ThisreportdefinedCPSasatightlycoupledcyberand physicalsystemsthatexhibitalevelofsmartintegration.Thesesystemshave computationalprocessesthatinteractwithphysicalcomponents.Therefore,a callforactionwasexpectedasthefutureapplicationsofCPSwereunderstood tobemoredisruptivethanITwasduringthethirdindustrialrevolution.
Bytheendof2012,thedigitalbranchofaUnitedStatescompany,General Electric(GE),coinedthetermIndustrialInternet,bringingtogethersmart machines,advancedanalytics,andpeopleatwork.GEdescribedthisintegration asanetworkofconnecteddevicesthatcanextract,transport,store,transform andsupplyvaluableactionableinsightsthatcanleveragefasterbusiness decisionsatindustrialcompanies,increasingtheircompetitiveness9 .
TheIIoTismainlyorientedtotheapplicationoftheIoT,machine-tomachine(M2M)communications,andindustrialbigdataanalyticstoindustry withaclearfocusonusingdatatogenerateaddedvalue.Inviewofthe needtosharedataandinformation,theIIoTapproachmovesthemajor IoTtechnologies,likesmartsensing,real-time,deterministicandwireless communications,sensorfusionfordatapreprocessing,artificialintelligencefor dataprocessinganddeliveryofinformation,intoindustry.Additionally,the IIoTapproachdefinesdifferentlayersoftechnologydeployment.Briefly,these deploymentlayersarethefollowing:
• Edge,wheretheelementsareneartheconnectedassets,whichisusefulfor real-timeanalyticsandcontroltechnologies.
• Cloud,wherethedataaresentouttocomputingservicesovertheInternet, whichisusefulforcomplexanalyticsanddatastorage.
Againstthisbackdrop,GE,togetherwithIBMandSAP,founded,inMarch 2014,theIndustrialInternetConsortium(IIC)10,withtheaimofbringing togetherthecompaniesandtechnologiesneededtospeedupthedevelopment, adoption,andwidespreadsharingofdataandinformation,smartanalytics, andpeopleatwork.AlthoughIIoTstartedoutasamainlyAmericaninitiative, theIIChasnowgoneglobalwithmorethan200membercompaniesaround theworld.
1.1.3OtherInternationalStrategies
Asexplainedin Section1.1.1 and Section1.1.2,theconceptof4IRhasbeen adoptedaroundtheworldinspiredbytheoriginalinitiativeslaunchedby
9 EverythingyouneedtoknowabouttheIndustrialInternetofThings. https://www.ge.com/digital/blog/everything-you-need-know-about-industrialinternet-things/ 10 http://www.iiconsortium.org/
GermanyandtheUSA.However,thereareseveralcountry-specificvariations. Someoftheseapproachesarebrieflydescribedbelow.
InFrance,4IRwasadoptedinApril2015astheIndustrieduFutur,whichis orientedtowardsthedigitaltransformationofFrenchindustry.Itisprimarilyan implementationoftheachievementsofEuropeanCommission(EU)initiatives suchasFactoryoftheFuture.IndustrieduFuturhasborrowedfivemain notionsfromtheEUinitiatives:(1)Developmentofthetechnologysupplyfor thefactoriesofthefutureinareaswhereFrancecanbecomealeaderinthe nextthreetofiveyearsbysupportinglargestructuralprojectsoutofindustry. Thesupplyoftechnologieswillbebasedonadditivemanufacturing,IoT, augmentedreality,etc.(2)Financialsupportforcompanies.(3)Trainingfor thenextgenerationofemployeesintheknowledgeandskillsneededtoapply newtechnologiesinthefactoriesofthefuture.(4)SupportforEuropeanand internationalcooperation,fosteringinnovationstrategiestogetherwithother Europeancountries,especiallyGermany,andotherinternationalalliances.(5) Promotionofactivitiesorientedtoshowcase4IR-relatedFrenchdevelopments andtechnologyknow-how.
InSpain,4IRadoptionisdrivenbyIndustriaConectadasupportedbythe MinistryofEconomy,IndustryandCompetitiveness.Inthiscase,theinitiative isdesignedtoprovidefinancialsupportandassistancetopromotethedigital transformationoftheSpanishindustrialsector.LikeIndustrieduFutur,the approachtakenbyIndustriaConectadaisalignedwiththeGermanIndustrie 4.0.However,ittakesaspecificbusinesssolutionapproachfocusingonbig dataandanalytics,cybersecurity,cloudcomputing,connectivityandmobility, additivemanufacturing,roboticsandembeddedsensorsandsystemsasthe mainareasofdevelopment.
InAsia,thereareseveralapproaches:MadeinChina2025,MadeinIndia andASEAN4.0fortheAssociationofSoutheastAsianNations(ASEAN) whosemembersincludetechnologydevelopmentleaderslikeSingaporeand Malaysia.AlltheseapproachesarealignedwithIndustry4.0anddesignedto pushforwardtheirrespectiveindustriesinordertoincreasecompetitiveness. Japan,ontheotherhand,hastakenadifferentapproachcalledSociety5.0. Society5.0isorientedtowardsthetransformationofsocietyintoasuper smartsociety.ThispolicyexpectsCPS,viewedasthekeyelementscapable ofcombiningcyberandphysicalspace,tobringaboutamajorsocietalshift. Machinesandartificialintelligencewillbethemainplayersinthisfifthstage ofsociety.
Inconclusion,the4IRismorethantechnologydevelopment:itisan industrialshiftinvolvingeconomic,technicalandsocietalcomponentsaimed atimprovingindustrialcompetitivenessatalllevelswithapotentialimpactall overtheworld.Thisrevolution,andthedifferentadoptedpolicies,isleveraging thesmartindustrydescribedin Section1.2
FIGURE1.3
Differentlevelsofindustrysmartization.
1.2IndustrySmartization
Thewordsmartizationisoftenusedtodescribeanevolutionaryshifttowards smartbehavior.Technologiesrelatedtothisevolutionaryshiftareenabling smartindustriesatdifferentlevels.Smartizationisamainthreadofthis bookusedtodescribehowmachinelearningisappliedtoprovidesmart capabilities.Therefore,wedefinefourdifferentlevelsofabstraction:component (Section1.2.1),whichispartofamachine(Section1.2.2)withinaproduction (Section1.2.3)facilitythathasneedofdistribution(Section1.2.4)capabilities tomoveproductstodifferentcustomers. Figure1.3 showsadiagramillustrating thisapproach.
1.2.1AttheComponentLevel
Asexplainedabove,therearedifferentlevelsofabstractioninindustrysmartization.Atthebottom,wehavemachinecomponents,e.g.,bearings,valves,ball screws,guidesandshafts.Component smartization referstotheintroduction ofcomponent self-awareness capabilitiestoprovidefailurediagnosisand prognosis.Thiscanhelptoincreasetheavailabilityofthewholesystemor subsystem,e.g.,machines,aircompressors,etc.Componentsaremadeselfawarebyembeddingsensorsintothecomponentstructure.Sensorcomplexity isdeterminedbytheamountofspaceavailableinthecomponent.Forexample, theremayberoomforavalvesensorandelectronicsinitsmanifold,buta ball-screwsensorneedstobeintegratedintoitsstructuralmaterial.
Themainaimoftheseembeddedsensorsistoextractdatarelatedto phenomenathatmaycausethecomponenttofail.Forexample,asensorbuilt intoabearingmighthavetomeasurevibration,temperature,lubrication,
humidity,presenceofmetallicparticles,etc.Thesedatacanbeprocessedat thesensororwithinanupperlayer,launchingalarmsrelatedtopotential failuresorremainingusefullife(RUL).Inthiscase,sensorsarereferredtoas smartsensors.
ComponentdataprocessingisdescribedinChapter4,whereballbearings areusedasatestingscenario.Chapter4basicallyillustrateswhatasmart componentisandhowitcancontributetotheoverallindustrysmartization.
1.2.2AttheMachineLevel
Thenextlevelofabstractioninindustryisthemachine.Inthiscase,there aretwosourcesofsmartcapabilities:(1)self-awarecomponentsthatare abletoprovidefailurediagnosisandprognosis,(2)dataaggregationfrom differentsmartcomponentsandsensorsthatareabletosupplycontextual characteristics,usefulforprovidingactionableinsightsaboutthesystemor subsystem.
Leeetal.(2014)explainedthattheIoThasenableddataavailability,where amachine,withthehelpofaCPS,isabletoextractenoughinformationtobe capableofself-assessment.Asavailabilityisthemostimportantissueforan industrialmachine,self-assessmentcapabilitiescanprovidethepast,current andfutureconditionsofthesubsystemstoenabletoolstoimprovethisissue throughmaintenanceandadaptivecontrol.
Therefore,aself-maintainedmachineisabletoassessitsownstateof healthandlevelofdegradation.Thisisusefulforpreventiveand predictive maintenance inordertoreducemachinedowntime,increasingitsavailability. Aself-awaremachineisabletousethedatatomonitorthecurrentoperatingconditionsandassessitsownbestoperationalstate,adjustingprocess parameterstoensurethehighestpossibleefficiency.
However,theconceptofsmartmachineisbroaderthantheusageofdatafor self-assessment.Asdescribedin Section1.1,oneofthekeyconceptsisdataand informationsharing.Inthisrespect, machine-to-machinecommunication (M2M)isaconceptdescribedbymanyauthors(e.g.,Leeetal.(2014),Linand Chen(2016),Lietal.(2016),Alietal.(2017)andTunaetal.(2017))who highlightdataandinformationsharingbetweenmachinesinordertoperform peer-to-peercomparison.Thiscanbeusefulfordetectingearlydegradation oranyothersituationlikelytoincreasemachineavailability.M2Misalso usefulforcreatinganetworkofcooperativesmartmachines,whereadaptable coordinationincreasesflexibilityandproductivity,implementingtheconcept ofsmartproductionsystem.
Chapter5describeshowmachinelearningisabletoleverageself-awareness capabilitiesinamachine.Inthiscase,theservomotorsmovingamachineaxis arestudiedasausecasescenarioofasmartmachinesubsystem.
Systemintegrationofaproductionsystem.
1.2.3AttheProductionLevel
Asmentionedin Section1.2.2,acollectionofnetworkedsmartmachinessharing dataandinformationcanbedefinedasasmartproductionsystem.Additionally, thismachineinterconnectionprovidesforassetfleetanalytics,suchas overall equipmentefficiency (OEE)definedbyavailability,productivity,energy efficiency,andmanufacturingquality.
Attheproductionlevel,theabstractionisdefinedasasmartmanufacturing system.Thissmartsystemisabletointegratesmartmachines,butalsodata comingfromotherdomainssuchasrawmaterialsbehavior,environment, energy,business,materialflowandotherkeyperformanceindicators(KPIs). Thisintegrationprovidesahigh-levelviewofthemanufacturingenvironment, wheredatacouldextractaddedvalueinformationthatcanhelptoincrease systemefficiency.
Therefore,asshownin Figure1.4,asmartfactoryisabletotakeadvantage ofwell-establishedintegrationsystems,suchasenterpriseresourceplanning (ERP),whichhasreal-timeandsynchronizedbusinessdata:purchasing,sales, distribution,finance,manufacturing,etc.Basedonthesedata,asmartfactory shouldmakebusiness-baseddecisionstoincreaseitscompetitiveness.Besides, manufacturingexecutionsystems(MES)areasourceofusefuldataforsmart factories.Inthiscase,aMESisabletoprovidedatarelatedtotheproduction system,trackingKPIs,rawmaterials,stock,etc.Programmablelogiccontrollers (PLC)andsupervisorycontrolanddataacquisition(SCADA)aresmartlayers ontopofmachinesthatareabletoprovidedirectcontrolandsupervisionof productionsystemsandmachines.
Toillustratemachinelearning-basedsmartizationattheproductionlevel, Chapter6showsausecasescenarioofanautomatedvisualinspectionsystem appliedtoaheattreatmentproductionsystem.
FIGURE1.4
1.2.4AttheDistributionLevel
Thenextlevelofabstractionisdistribution,whereproductsaresentto customersorotherparentfactories.Thislevelisdefinedassmartlogistics. Atthispoint,aggregateddatacomingfromdifferentproductionsystemsare mixedwithdistributiondatainordertoincreasesystemefficiency,i.e.,deliver theproductattherighttime.
Distributionsystemsarefirstandforemostcomplexcombinationsofinfrastructureandresourceswithlimitedavailability,anddifferentproduct destinationsandrequireddeliverytimes.Asaresult,processesshouldbe highlyefficienttoavoidbottlenecksandreduceproducttimestomarket (lengthoftimethatittakesfromaproductbeingconceiveduntilitreaches thecustomer)topreventanegativeimpactoncompanycompetitiveness.
Therefore,smartdistributionsystemsarecomplexresourcemanagersthat areabletoperformthreedifferentactivities:automatedplanning,implementationandcontrol.Basedontheproductiondata,thesmartsystemdefines adeliveryplanfortheproduct,includingitsdestination,requiredinfrastructureandresources(e.g.,airports,airplanes,etc.)andcontingencyplanning. Planningisperformedbysearchingtheoptimumpathandtakingintoaccount otherfactors,suchasdifferentproductsusingthesameresourcestomaximize theirusage.Also,thesmartsystemimplementsorexecutestheplan,measuringitspast,currentandfuturestatesinordertodetectpossibledeviations andproducetheactionableinsightsbasedonthecontingencyplan.Ifsuch deviationsaredetected,thesmartsystemisabletocontrolthesituationand taketherequiredactionstoguaranteesystemquality.
Chapter7illustratesanapplicationofmachinelearningtoenablesmart logisticsusingausecaserelatedtoairfreight.
1.3MachineLearningChallengesandOpportunities withinSmartIndustries
TheIndustrialInternetisexpectedtoincreaseindustrialefficiencyby3.3% peryearwithsavingsofaround2.6%withrespecttocostreduction11.These figureswillbetheresultofanoverallefficiencyincrease,leadingtohigherproductionwithlowerrawmaterialandenergyusage.Asdescribedin Section1.2, developmentsaremovingtowardssmartization,whereartificialintelligenceis abigplayer.
Additionally,theinvestmentinIndustrialInternetapplicationsismeasured inbillionsdependingontheregion.Forexample,theexpectedinvestmentin Europeisaround €140billionperyear.Thismeansthatindustry-oriented
11 Industry4.0-OpportunitiesandchallengesoftheIndustrialInternet. https://www.pwc.nl/en/assets/documents/pwc-industrie-4-0.pdf
artificialintelligence-basedproductswillreceivestrongsupport,boostingthe adoptionrate.Machinelearningisanartificialintelligencetechnologywith promisingapplicationsenablingsmartmanufacturingsystemswithinthe4IR. Ithasahugepotentialatalllevels:business,technologyandpeople.However, therearesomechallengesandopportunitiesrelatedtoeachlevel.
Inordertounderstandthechallengesandopportunitiesformachinelearning inindustry,weshouldlookathowitfitsintoa4IRarchitecture.Todothis, weusethereferencearchitectureforIIoT(Linetal.,2017).Thisarchitecture definesthreedifferenttiersasfollows:
• Edgetier collectsthedatasourcedfromdifferentindustriallevels:component,machine,productionlineorlogistics(see Section1.2).
• Platformtier processesthedatafromtheedgetierandprovidesafirst layerofservicesandfeedback,wheretimeisacriticalvariableforsecurity andintegrityreasons.
• Enterprisetier collectsinformationfromtheplatformtieranddeploysa secondlayerofservicesthatprovidessupportforhigh-leveldecisionmaking.
Figure1.5 illustratesanimplementationofthisarchitecture.Thisexample addressespredictiveassetmaintenance,wheredifferentcomponentdata,suchas thecomputernumericalcontrol(CNC)andsmartenergymeasurementsensors (e.g.,OberonX)amongothers,arecollected,transmittedandsynchronized intheedgetier.Then,allthedataaresenttotheplatformtierwherea machinelearninglayerextractscriticalactionableinsightsthatcanbeusedto stopthemachineintheeventofanemergencyortosupportdecisionmaking bythemachineoperatorintheeventofahighlikelihoodoffailure.Part ofthisinformationistransmittedtothenexttier,whereanothermachine learninglayerextractsoperationalorbusiness-orientedinsights.Inthislayer, businessdecisionsaremadebasedonthesuppliedactionableinsights,suchas productionforecastingoroverallfactoryavailability.
Asmartindustryarchitecturecouldbemappedtodifferentimpactlevels byanalyzingthemainimplicationsofmachinelearning.Therefore,themain impactofthemachinelearningusedintheenterprisetierisrelatedtopeople andbusiness.Themachinelearningappliedintheplatformtierwillhaveless impactonbusinessandattachmoreimportancetotechnology.Intheedgetier, machinelearningwillhaveadirectimpactasthemainorchestratorofeach smartelementwithinthistier.Thefollowingsectionsgiveageneralideaofthe expectedimpactateachlevelwiththeirrelatedchallengesandopportunities.
1.3.1ImpactonBusiness
Asdescribedabove,machinelearningwillbeakeyenablerforsmartindustries withimportantlevelsofOEE,whichwillhaveapositiveimpactonbusiness competitiveness.Anincreaseincompetitivenessmeansthatthegoodsproduced
FIGURE1.5
Roleofmachinelearningwithinsmartfactorypredictivemaintenance12 .
bysmartindustrieshavearelativelyshortertime-to-marketthancompetitor products,thatis,thesupplyoftherightproductattherighttimeandatthe rightprice.
Conceptslikemasscustomizationorservitizationhaveemergedtoachieve thislevelofimprovement.Masscustomizationisrelatedtotheabilityof extremelyflexibleproductionsystemstoprovidehighvolumesofcustomized productsandservices.Toachieveflexibility,theproductionsystemhastobe awareofthepastandpresentconditionstomonitoractualavailability,aswell asfutureconditionsinordertopredictrequiredproductionsystemchanges relatedtoanewproductcustomization.
However,assetsmartizationhasadisruptiveimpactonbusiness,called servitization.AsdescribedbyKampetal.(2017),smartizationwillleverage newbusinessmodelssuchaspredictivemaintenance,qualitycontrol,plantfloorefficiency,etc.,whichtakeadvantageofpredictiveanalyticsoutputby machinelearning.Additionally,increasedavailabilitywillenablethemodelof sellinguptimeinsteadofmachines.Suchmodelsarebeingappliedinsectors liketheaerospaceindustry,whereairplaneturbinesaresoldbyflighttime. Therearegoodopportunitiesformachinelearningtoinfluenceandincrease businesscompetitiveness,buttherearesomechallengesthatithastoovercome. Themostimportantchallengeistounderstandthatmachinelearningisnot alow-costtechnology:anelaborateimplementationstrategyisrequiredto understandhowtogetthefastestreturnoninvestmentandmakethebiggest impactontheenterprise.Nevertheless,effortsarebeingundertakenfrommany sidesaimedatreducingtheexpensiveresourcesrequiredbymachinelearning, suchasdatastorageandtrainingtime.Someexamplesareone-shotalgorithms capableoflearningfromasingleexample(Fei-Feietal.,2006),datastream
12 http://www.iiconsortium.org/smart-factory-machine-learning.htm
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Miniver loved the Medici, Albeit he had never seen one; He would have sinned incessantly Could he have been one
Miniver cursed the commonplace, And eyed a khaki suit with loathing; He missed the mediæval grace Of iron clothing
Miniver scorned the gold he sought, But sore annoyed he was without it; Miniver thought and thought and thought And thought about it
Miniver Cheevy, born too late, Scratched his head and kept on thinking; Miniver coughed, and called it fate, And kept on drinking.
TWO MEN
There be two men of all mankind That I should like to know about; But search and question where I will, I cannot ever find them out
Melchizedek he praised the Lord, And gave some wine to Abraham; But who can tell what else he did Must be more learned than I am
Ucalegon he lost his house When Agamemnon came to Troy; But who can tell me who he was— I’ll pray the gods to give him joy
There be two men of all mankind That I’m forever thinking on; They chase me everywhere I go,— Melchizedek, Ucalegon.
Arthur Guiterman, among the best of our present day humorous writers, never did anything better than this intensified bit of
MAVRONE
ONE OF THOSE SAD IRISH POEMS, WITH NOTES
From Arranmore the weary miles I’ve come;
An’ all the way I’ve heard
A Shrawn[2] that’s kep’ me silent, speechless, dumb, Not sayin’ any word
An’ was it then the Shrawn of Eire,[3] you’ll say, For him that died the death on Carrisbool? It was not that; nor was it, by the way,
The Sons of Garnim[4] blitherin’ their drool; Nor was it any Crowdie of the Shee,[5]
Or Itt, or Himm, nor wail of Barryhoo[6]
For Barrywhich that stilled the tongue of me.
’Twas but my own heart cryin’ out for you
Magraw![7] Bulleen, shinnanigan, Boru, Aroon, Machree, Aboo![8]
ELEGY
The jackals prowl, the serpents hiss In what was once Persepolis. Proud Babylon is but a trace Upon the desert’s dusty face. The topless towers of Ilium Are ashes Judah’s harp is dumb
The fleets of Nineveh and Tyre Are down with Davy Jones, Esquire
And all the oligarchies, kings, And potentates that ruled these things Are gone! But cheer up; don’t be sad; Think what a lovely time they had!
Oliver Herford, born in England but living most of his life in America, has without doubt the most humorous soul in the world.
His art, which is pictorial as well as literary, is unique and of an intangible, indescribable nature.
As graceful of fancy as Spenser, as truly funny as Sir William Gilbert, he also possesses a deep philosophy and a perfect technique.
PHYLLIS LEE
Beside a Primrose ’broider’d Rill
Sat Phyllis Lee in Silken Dress
Whilst Lucius limn’d with loving skill
Her likeness, as a Shepherdess Yet tho’ he strove with loving skill
His Brush refused to work his Will
“Dear Maid, unless you close your Eyes I cannot paint to-day,” he said; “Their Brightness shames the very Skies And turns their Turquoise into Lead.”
Quoth Phyllis, then, “To save the Skies And speed your Brush, I’ll shut my Eyes.”
Now when her Eyes were closed, the Dear, Not dreaming of such Treachery, Felt a Soft Whisper in her Ear,
“Without the Light, how can one See?”
“If you are sure that none can see I’ll keep them shut,” said Phyllis Lee.
SOME GEESE
Ev-er-y child who has the use
Of his sen-ses knows a goose
See them un-der-neath the tree
Gath-er round the goose-girl’s knee, While she reads them by the hour From the works of Scho-pen-hau-er.
How pa-tient-ly the geese at-tend! But do they re-al-ly com-pre-hend
What Scho-pen-hau-er’s driv-ing at?
Oh, not at all; but what of that?
Nei-ther do I; nei-ther does she; And, for that mat-ter, nor does he.
THE CHIMPANZEE
Children, behold the Chimpanzee: He sits on the ancestral tree
From which we sprang in ages gone
I’m glad we sprang: had we held on, We might, for aught that I can say, Be horrid Chimpanzees to-day
THE HEN
Alas! my Child, where is the Pen
That can do Justice to the Hen?
Like Royalty, She goes her way, Laying foundations every day, Though not for Public Buildings, yet For Custard, Cake and Omelette.
Or if too Old for such a use
They have their Fling at some Abuse, As when to Censure Plays Unfit
Upon the Stage they make a Hit, Or at elections Seal the Fate
Of an Obnoxious Candidate
No wonder, Child, we prize the Hen, Whose Egg is Mightier than the Pen
MARK TWAIN: A PIPE DREAM
Well I recall how first I met Mark Twain an infant barely three Rolling a tiny cigarette While cooing on his nurse’s knee
Since then in every sort of place
I’ve met with Mark and heard him joke, Yet how can I describe his face? I never saw it for the smoke
At school he won a smokership, At Harvard College (Cambridge, Mass )
His name was soon on every lip, They made him “smoker” of his class
Who will forget his smoking bout With Mount Vesuvius our cheers When Mount Vesuvius went out
And didn’t smoke again for years?
The news was flashed to England’s King, Who begged Mark Twain to come and stay, Offered him dukedoms anything To smoke the London fog away
But Mark was firm. “I bow,” said he, “To no imperial command, No ducal coronet for me, My smoke is for my native land!”
For Mark there waits a brighter crown! When Peter comes his card to read He’ll take the sign “No Smoking” down, Then Heaven will be Heaven indeed
Some take their gold In minted mold, And some in harps hereafter, But give me mine In tresses fine, And keep the change in laughter!
AFTER HERRICK SONG
Gather Kittens while you may, Time brings only Sorrow; And the Kittens of To-day Will be Old Cats To-morrow
THE PRODIGAL EGG
An egg of humble sphere By vain ambition stung, Once left his mother dear When he was very young.
’Tis needless to dilate Upon a tale so sad; The egg, I grieve to state, Grew very, very bad
At last when old and blue, He wandered home, and then They gently broke it to The loving mother hen
She only said, in fun, “I fear you’re spoiled, my son!”
Frank Gelett Burgess, one time editor of The Lark, a short-lived humorous periodical, is at his best in the realms of sheer nonsense. His Purple Cow has a nation-wide reputation and his humorous excursions into the French Forms are always marked by exact precision as to rule and law
THE PURPLE COW
I never saw a Purple Cow, I never hope to see one; But I can tell you, anyhow, I’d rather see than be one.
THE INVISIBLE BRIDGE
I’d Never Dare to Walk across A Bridge I Could Not See; For Quite afraid of Falling off, I fear that I Should Be!
VILLANELLE OF THINGS AMUSING
These are the things that make me laugh
Life’s a preposterous farce, say I! And I’ve missed of too many jokes by half
The high-heeled antics of colt and calf, The men who think they can act, and try
These are the things that make me laugh
The hard-boiled poses in photograph, The groom still wearing his wedding tie
And I’ve missed of too many jokes by half!
These are the bubbles I gayly quaff
With the rank conceit of the new-born fly
These are the things that make me laugh!
For, Heaven help me! I needs must chaff, And people will tickle me till I die And I’ve missed of too many jokes by half!
So write me down in my epitaph
As one too fond of his health to cry
These are the things that make me laugh, And I’ve missed of too many jokes by half!
PSYCHOLOPHON
Supposed to be Translated from the Old Parsee
Twine then the rays Round her soft Theban tissues!
All will be as She says, When that dead past reissues. Matters not what nor where, Hark, to the moon’s dim cluster!
How was her heavy hair
Lithe as a feather duster! Matters not when nor whence; Flittertigibbet! Sounds make the song, not sense, Thus I inhibit!
Carolyn Wells has written much humorous verse and prose. Her work has appeared in many of the periodicals and in book form.
THE IDIOT’S DELIGHT
A curious man of the human clan
Is a man who fools himself; Who thinks he can swing the Pierian spring Through a conduit of books on a shelf! Who thinks if he pores in the old bookstores And browses among the rares, He is fit to belong to the scholarly throng And gives himself scholarly airs.
He gasps as he speaks of his worn antiques With emotion almost dumb!
Or he solemnly turns his Kilmarnock Burns With an awed and reverent thumb; He’ll scrimp to possess a Kelmscott Press,
And hoard up his hard-earned wage Till he saves the cost of a Paradise Lost With the right sort of title page
If he has on his shelves some dumpy twelves, Of which he’s a connoisseur, The bibliophile, with a fatuous smile, Believes he’s a littérateur! Because he achieves incunabula leaves, On himself as a scholar he’ll look; Though I’m ready to bet no scholar I’ve met Has ever collected a book!
The difference, you see, in the viewpoint must be, And it is a distinction nice; A scholar will look at the worth of a book, A collector will think of its price. He nearly bursts with pride in his firsts; And you can’t get it into his dome That he cannot affect his intellect By buying a tattered tome!
A collector may have matter gray, He may have wisdom, too; As he may have a head of a carroty red Or eyes of a chicory blue. But he has these things by the grace of God; Especially his good looks; By Nature’s laws, and not because The things he collects are books!
And so I maintain there is no brain, No genius or talent or mind, Required to look for a certain book, Or to struggle that book to find No collector reads his precious screeds, He appraises his books by sight; And I make claim that the blooming game Is the idiot’s delight!
THE MYSTERY
I can understand politics, civics and law, Of national issues I have no great awe;
The theories of Einstein are simple to me, And psychoanalysis mere A. B. C. But there is one thing I can’t get in my head Why do people marry the people they wed?
I can do mathematics, no matter how high; And to me fourth dimension is easy as pie; Most intricate problems I readily solve, And I know why the nebular spirals revolve But on this baffling question no light has been shed; Why do people marry the people they wed?
Long hours over Nietzsche I frequently spend, I’ve all his philosophy at my tongue’s end Of Freudian conclusions I haven’t a doubt I’ve got human complexes all straightened out. But on this deep problem I muse in my bed Why do people marry the people they wed?
I’ve studied up ancient religions and cults, I’ve tried spiritism with curious results; I know the Piltdown and Neanderthal man, How big is Betelgeuse and how old is Ann; But this I shall wonder about till I’m dead Why do people marry the people they wed?
WOMAN
Women are dear and women are queer
Men call them, with a laugh, The female of the species, Or a husband’s better half. They sing their praise in many ways, They flatter them but, oh, How little they know of Woman
Who only women know!
Now women are pert and women will flirt, And they’re catty and rude and vain; And sometimes they’re witty and sometimes they’re pretty And sometimes they’re awfully plain. But Woman is rare beyond compare, The poets tell us so; How little they know of Woman
Who only women know!
Women are petty and women are fretty, They try to hide their years; They steadily nag and nervously rag, And frequently burst into tears
But Woman is gracious, serene and calm, Above all tricks or arts, Her sympathy’s like a soothing balm To sad and sorrowing hearts
Women are very perverse and contrary, They will contradict you flat; Oh, women I’ll call the devil and all, There’s no denying that!
But Woman, oh, men, is beyond our ken, Too angelic for mortals below; How little they know of Woman Who only women know!
A SYMPOSIUM OF POETS
Once upon a time a few of the greatest Poets of all ages gathered together for the purpose of discussing the merits of the Classic Poem:
Peter, Peter, Pumpkin Eater, Had a wife and couldn’t keep her, Put her in a Pumpkin shell, And there he kept her very well
In many ways this historic narrative called forth admiration. One must admit Peter’s great strength of character, his power of quick decision, and immediate achievement. Some hold that his inability to retain the lady’s affection in the first place, argues a defect in his nature; but remembering the lady’s youth and beauty (implied by the spirit of the whole poem), we can only reiterate our appreciation of the way he conquered circumstances, and proved himself master of his fate, and captain of his soul! Truly, the Pumpkin-Eaters must have been a forceful race, able to defend their rights and rule their people.
The Poets at their symposium unanimously felt that the style of the poem, though hardly to be called crude, was a little bare, and they took up with pleasure the somewhat arduous task of rewriting it.
Mr. Ed. Poe opined that there was lack of atmosphere, and that the facts of the narrative called for a more impressive setting. He therefore offered:
The skies, they were ashen and sober, The lady was shivering with fear; Her shoulders were shud’ring with fear. On a dark night in dismal October, Of his most Matrimonial Year. It was hard by the cornfield of Auber, In the musty Mud Meadows of Weir, Down by the dank frog-pond of Auber, In the ghoul-haunted cornfield of Weir
Now, his wife had a temper Satanic, And when Peter roamed here with his Soul, Through the corn with his conjugal Soul, He spied a huge pumpkin Titanic, And he popped her right in through a hole Then solemnly sealed up the hole
And thus Peter Peter has kept her Immured in Mausoleum gloom, A moist, humid, damp sort of gloom. And though there’s no doubt he bewept her, She is still in her yellow hued tomb, Her unhallowed, Hallowe’en tomb And ever since Peter side-stepped her, He calls her his lost Lulalume, His Pumpkin-entombed Lulalume.
This was received with acclaim, but many objected to the mortuary theory
Mrs. Robert Browning was sure that Peter’s love for his wife, though perhaps that of a primitive man, was of the true Portuguese stamp, and with this view composed the following pleasing Sonnet:
How do I keep thee? Let me count the ways. I bar up every breadth and depth and height My hands can reach, while feeling out of sight For bolts that stick and hasps that will not raise. I keep thee from the public’s idle gaze, I keep thee in, by sun or candle light.
I keep thee, rude, as women strive for Right. I keep thee boldly, as they seek for praise, I keep thee with more effort than I’d use
To keep a dry-goods shop or big hotel.
I keep thee with a power I seemed to lose
With that last cook I’ll keep thee down the well, Or up the chimney-place! Or if I choose, I shall but keep thee in a Pumpkin shell
This was of course meritorious, though somewhat suggestive of the cave-men, who, we have never been told, were Pumpkin Eaters.
Austin Dobson’s version was really more ladylike:
BALLADE OF A PUMPKIN
Golden-skinned, delicate, bright, Wondrous of texture and hue, Bathed in a soft, sunny light, Pearled with a silvery dew. Fair as a flower to the view, Ripened by summer’s soft heat, Basking beneath Heaven’s blue, This is the Pumpkin of Pete
Peter consumed day and night, Pumpkin in pie or in stew; Hinted to Cook that she might Can it for winter use, too Pumpkin croquettes, not a few, Peter would happily eat; Knowing content would ensue, This is the Pumpkin of Pete.
Everything went along right, Just as all things ought to do; Till Peter, unfortunate wight, Married a girl that he knew, Each day he had to pursue, His runaway Bride down the street, So her into prison he threw, This is the Pumpkin of Pete
Lady, a sad lot, ’tis true, Staying your wandering feet; But ’tis the best place for you, This is the Pumpkin of Pete.
L’envoi
Like the other women present Dinah Craik felt the pathos of the situation, and gave vent to her feelings in this tender burst of song:
Could I come back to you Peter, Peter, From this old pumpkin that I hate; I would be so tender, so loving, Peter,— Peter, Peter, gracious and great
You were not half worthy of me, Peter, Not half worthy the like of I; Now all men beside are not in it, Peter,— Peter, Peter, I feel like a pie.
Stretch out your hand to me, Peter, Peter, Let me out of this Pumpkin, do; Peter, my beautiful Pumpkin Eater, Peter, Peter, tender and true.
Mr. Hogg took his own graceful view of the matter, thus:
Lady of wandering, Blithesome, meandering, Sweet was thy flitting o’er moorland and lea; Emblem of restlessness, Blest be thy dwelling place, Oh, to abide in the Pumpkin with thee.
Peter, though bland and good,
Never thee understood, Or he had known how thy nature was free; Goddess of fickleness, Blest be thy dwelling place, Oh, to abide in the Pumpkin with thee
Mr. Kipling grasped at the occasion for a ballad in his best vein. The plot of the story aroused his old time enthusiasm, and he transplanted the pumpkin eater and his wife to the scenes of his earlier powers:
In a great big Mammoth pumpkin
Lookin’ eastward to the sea, There’s a wife of mine a-settin’
And I know she’s mad at me. For I hear her calling, “Peter!”
With a wild hysteric shout; “Come you back, you Punkin Eater, Come you back and let me out!”
For she’s in a punkin shell, I have locked her in her cell; But it really is a comfy, well-constructed punkin shell; And there she’ll have to dwell, For she didn’t treat me well, So I put her in the punkin and I’ve kept her very well.
Algernon Swinburne was also in one of his early moods, and as a result he wove the story into this exquisite fabric of words:
IN THE PUMPKIN
Leave go my hands. Let me catch breath and see, What is this confine either side of me?
Green pumpkin vines about me coil and crawl, Seen sidelong, like a ’possum in a tree, Ah me, ah me, that pumpkins are so small!
Oh, my fair love, I charge thee, let me out; From this gold lush encircling me about; I turn and only meet a pumpkin wall. The crescent moon shines slim, but I am stout, Ah me, ah me, that pumpkins are so small!
Pumpkin seeds like cold sea blooms bring me dreams; Ah, Pete, too sweet to me, my Pete, it seems Love like a Pumpkin holds me in its thrall; And overhead a writhen shadow gleams, Ah me, ah me, that pumpkins are so small!
This intense poesy thrilled the heavens, and it was with a sense of relief to their throbbing souls that they listened to Mr. Bret Harte’s contribution:
Which I wish to remark, That the lady was plain; And for ways that are dark And for tricks that are vain, She had predilections peculiar, And drove Peter nearly insane.
Far off, anywhere, She wandered each day; And though Peter would swear, The lady would stray; And whenever he thought he had got her, She was sure to be rambling away
Said Peter, “My Wife, Hereafter you dwell For the rest of your life
In a big Pumpkin Shell ” He popped her in one that was handy, And since then he’s kept her quite well
Which is why I remark, Though the lady was plain, For ways that are dark And tricks that are vain, A husband is very peculiar, And the same I am free to maintain.
Oscar Wilde in a poetic fervour and a lily-like kimono, recited with tremulous intensity this masterpiece of his own:
Oh, Peter! Pumpkin-fed and proud, Ah me! ah me!
(Sweet squashes, mother!)
Thy woe knells like a stricken cloud; (Ah me; ah me!
Hurroo, Hurree!)
Lo! vanisht like an anguisht wraith; Ah me! ah me!
(Sweet squashes, mother!)
Wan hope a dolorous Musing saith; (Ah me; ah me!
Dum diddle dee!)
Hist! dare we soar? The Pumpkin shell Ah me! ah me!
(Sweet squashes, mother!)
(Fast and forever! Sooth, ’tis well. (Ah me; ah me!
Faloodle dee!)
There was little to be said after this, so the meeting was closed with a solo by Lady Arthur Hill, using with a truly touching touch:
In the pumpkin, oh, my darling, Think not bitterly of me; Though I went away in silence, Though I couldn’t set you free. For my heart was filled with longing, For another piece of pie; It was best to leave you there, dear, Best for you and best for I
Two of our most gentle and kindly humorists may not be quoted, because it would be a crime to separate their text and pictures.
Peter Newell and J. G. Francis have drawn some of the most delicately witty pictures and have written quatrains or Limericks to accompany them, but picture and text must be shown together, if at all.
For the same reason our cartoonists may not be touched upon.
Nor can we include any writers whose work did not appear before 1900.
The scope of this book is bounded by the twentieth century, and much as we should like to present the Columnists and the more recent versifiers, they must be left for a later chronicler.
INDEX
About a Woman’s Promise, Unknown, 172
A S C , Burdensome Wife, A (from Hie! Fie!), 413
Donkey’s Voice, The (from Judas, the Arch-Rogue), 412
St. Anthony’s Sermon to the Fishes, 413
A I ,
Parody on Hafiz, 154
Academy of Syllographs, The, Count Giacomo Leopardi, 616
Acrostics, Sir John Davies, 309
A , J Q ,
To Sally, 650
A , J , 421
Will of a Virtuoso, The (from The Tatler), 422
Address to Bacchus, An, Marc-Antoine Gerard, 392
Address to the Toothache, Robert Burns, 444
A , G ,
Cocktail, The (from The Sultan of Sulu), 722
Fable of the Caddy Who Hurt His Head While Thinking, The, 723
Adventures of Baron Münchausen, (selections), Rudolph Erich Raspe, 589
Advice to a Friend on Marriage, Eustache Deschampes, 315
Advice to an Innkeeper, José Morell, 412
Advice to Ponticus, Johannes Audœmus, 194
Æ ’ Fables, 44
Lion, the Bear, the Monkey and the Fox, The, 44
Partial Judge, The, 45
Æ , G. W . See Lanigan, George Thomas
Æstivation, Oliver Wendell Holmes, 666
After a Wedding (from Mrs. Partington), Benjamin Penhallow Shillaber, 664
After Herrick: Song, Oliver Herford, 747
After Swimming the Hellespont, Lord Byron, 462
Against Abolishing Christianity, Jonathan Swift, 415
A , Grammar and Medicine, 76
Alarmed Skipper, The, James Thomas Fields, 668
A , B , Sleep, 359
A , T B , 683
A , Epigrams, 69
A B A B M , To the Vizier Cassim Obid Allah, on the Death of One of His Sons, 191
American humor, 643–760
A , E , Tooth for Tooth, 623
A , Epitaph, An, 77
Analects of Confucius, The (extracts), 156
A , Epigrams, 68
A , F. See Guthrie, T. A.
Anthologies, 311
A , 66 Epigrams, 67
A , Epigrams, 85
Apology for Cider, Olivier Basselin, 317
Apology for Herodotus (Noodle Stories from), Henry Stephens (Henri Estienn), 215
A , Metamorphose, or The Golden Ass (extracts), 112
Arabian humor, 33, 126–138, 208
Arabian Nights’ Entertainment, The, 33, 126
Bakbarah’s Visit to the Harem, 132
Husband and the Parrot, The, 131
Ignorant Man Who Set Up for a Schoolmaster, The, 129
Simpleton and the Sharper, The, 127
Thief Turned Merchant and the Other Thief, The, 128 Arabian Riddle, 35
