04317nam 2201189z- 450 9910619469103321202210253-0365-5174-3(CKB)5670000000391583(oapen)https://directory.doabooks.org/handle/20.500.12854/93169(oapen)doab93169(EXLCZ)99567000000039158320202210d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDeep Learning-Based Machinery Fault DiagnosticsMDPI - Multidisciplinary Digital Publishing Institute20221 online 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.History of engineering & technologybicsscTechnology: general issuesbicsscabnormal case removalalumina concentrationaluminum reduction processanti-noiseattention mechanismautonomous underwater vehicleauxiliary modelBayesian networkbearing fault detectionbelief rule basecanonical correlation analysiscanonical variate analysiscase-based reasoningconvolution fusionconvolutional neural networkdata augmentationdata-drivendeep residual networkdistributed predictive controldisturbance detectiondynamic autoregressive latent variables modeldynamicsevent-triggered controlevidential reasoningevidential reasoning rulefault detectionfault diagnosisfault tolerant controlfilterflywheel fault diagnosisfractional-order calculus theoryfuzzy fault tree analysisgated recurrent unitgearbox fault diagnosishammerstein output-error systemshigh-speed trainsinformation transformationintelligent fault diagnosisinterval type-2 Takagi-Sugeno fuzzy modeljust-in-time learningk-nearest neighbor analysislocal outlier factorLSSVMmulti-innovation identification theoryn/anonlinear networked systemsocean currentsoperational optimizationparameter optimizationpower transmission systemprocess monitoringPSOrobust optimizationsintering processspatiotemporal feature fusionstacked pruning sparse denoising autoencoderstate identificationstatistical local analysissubspace identificationsystem modellingthruster fault diagnosticsvariable time lagwavelet mutationHistory of engineering & technologyTechnology: general issuesChen Hongtianedt1063057Zhong KaiedtRan GuangtaoedtCheng ChaoedtChen HongtianothZhong KaiothRan GuangtaoothCheng ChaoothBOOK9910619469103321Deep Learning-Based Machinery Fault Diagnostics3013732UNINA