04280nam 2201165z- 450 991061946910332120231214132934.03-0365-5174-3(CKB)5670000000391583(oapen)https://directory.doabooks.org/handle/20.500.12854/93169(EXLCZ)99567000000039158320202210d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDeep Learning-Based Machinery Fault DiagnosticsMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (290 p.)3-0365-5173-5 This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis.Technology: general issuesbicsscHistory of engineering & technologybicsscprocess monitoringdynamicsvariable time lagdynamic autoregressive latent variables modelsintering processhammerstein output-error systemsauxiliary modelmulti-innovation identification theoryfractional-order calculus theorycanonical variate analysisdisturbance detectionpower transmission systemk-nearest neighbor analysisstatistical local analysisintelligent fault diagnosisstacked pruning sparse denoising autoencoderconvolutional neural networkanti-noiseflywheel fault diagnosisbelief rule basefuzzy fault tree analysisBayesian networkevidential reasoningaluminum reduction processalumina concentrationsubspace identificationdistributed predictive controlspatiotemporal feature fusiongated recurrent unitattention mechanismfault diagnosisevidential reasoning rulesystem modellinginformation transformationparameter optimizationevent-triggered controlinterval type-2 Takagi-Sugeno fuzzy modelnonlinear networked systemsfiltergearbox fault diagnosisconvolution fusionstate identificationPSOwavelet mutationLSSVMdata-drivenoperational optimizationcase-based reasoninglocal outlier factorabnormal case removalbearing fault detectiondeep residual networkdata augmentationcanonical correlation analysisjust-in-time learningfault detectionhigh-speed trainsautonomous underwater vehiclethruster fault diagnosticsfault tolerant controlrobust optimizationocean currentsTechnology: general issuesHistory of engineering & technologyChen Hongtianedt1063057Zhong KaiedtRan GuangtaoedtCheng ChaoedtChen HongtianothZhong KaiothRan GuangtaoothCheng ChaoothBOOK9910619469103321Deep Learning-Based Machinery Fault Diagnostics3013732UNINA