简介：ispaperestablishesaformalmodelforhybriddiagnosis,novelfeaturesincluding:(1)Itprovidesaunifiedtheoreticalframeworkforutilizingdevicemodelsandheuristicsindiagnosis,whichnaturallyintegratesalltheimportantcomponentsofdiagnosis-thestructuralandbehavioraldescriptionofdevices,faultmodes,thelowerandupperfaultbounds,faultpossibilitiesandheuristicrules-intoadiagnosticsystem.Devicemodelspredictoutputsfrominputs,heuristicrulesinferthepossibilitiesofcertaincomponentsbeingfaultyfromsymptons,andyetthecombinationofbothconstrainseachotherforustoreducethehypothesisspace.(2)Itpresentsatypicalwayofmodelingbehaviorofdevices,towhichthekeyistheintroductionofI-Ofunctionswithindefiniteinputs/outputs.(3)Itcaneasilybeimplementedoveraforware-chaininginferenceengine.

简介：IncreasingICdensitiesnecessitatediagnosismethodologieswithenhanceddefectlocatingcapabilities.YetthecomputationaleffortexpendedinextractingdiagnosticinformationandthestringentstoragerequirementsconstitutemajorconcernsduetothetremendousnumberoffaultsintypicalICs.Inthispaper,weproposeanRT-leveldiagnosismethodologycapableofrespondingtothesechallenges.Intheproposedscheme,diagnosticinformationiscomputedonagroupedfaulteffectbasis,enhancingboththestorageandthecomputationalaspects.Thefaulteffectgroupingcriteriaareidentifiedbasedonamodulestructureanalysis,improvingthepropagationabilityofthediagnosticinformationthroughRTmodules.Experimentalresultsshowthattheproposedmethodologyprovidessuperiorspeed-upsandsignificantdiagnosticinformationcompressionatnosacrificeindiagnosticresolution,comparedtotheexistinggate-leveldiagnosisapproaches.

简介：

简介：柔韧的差错诊断为非线性的系统的一个类基于适应观察员被学习直到输出注射。适应差错更新法律被设计保证诊断系统的稳定性。州的评价错误的上面的界限；适应观察员的差错评价错误分别地被给；在差错评价精确性上的适应更新法律的参数的效果也被讨论。模拟例子表明建议方法的有效性；分析结果。

简介：

简介：

简介：

简介：

简介：Inthispaper,byusingthewell-knownhigh-gainobserverdesign,anupdatelawforthegainandanadaptiveestimationofparameters,anewmethodoffaultdiagnosisforaclassofnonlinearsystemsispresented.Withoutresorttoanytransformationfortheparameters,theestimationerrorsofthestatesandtheparametersareguaranteedtobegloballyexponentiallyconvergentbyapersistentexcitationcondition.Comparedtotheexistingresults,itcanbeappliedtononlinearsystemswithnonlineartermsadmittinganincrementalratedependingonthemeasuredoutput.Acasestudyfurtherverifiesthevalidityoftheproposedresearch.

简介：基于结构化的内核主管部件，分析(KPCA)为非线性的进程被建议的一个新传感器差错诊断方法。由在变量的子集上执行KPCA，一套结构化的剩余，即，KPCA的放大力量，能以象部分PCA的一样的方法被获得。结构化的剩余在为传感器差错诊断填写一个隔离计划被利用，根据一个适当地设计的发生矩阵。传感器差错敏感和批评敏感被定义，基于哪个一个发生矩阵优化算法被建议改进结构化的KPCA的表演。建议方法的有效性在模仿的连续搅动的坦克反应堆(CSTR)上被表明过程。

简介：