A hybrid data-driven prognostic scheme based on unsupervised health indicator construction and random-effects Wiener process
Junyu Guo, Yuhang Song, Zhiyuan Wang, Tingxia Ma, Yang Xiao, Zifei Xu
Topics & Concepts
AutoencoderInterpretabilityComputer scienceArtificial intelligenceMachine learningConstruct (python library)Process (computing)Reliability (semiconductor)Data miningScheme (mathematics)Feature (linguistics)Unsupervised learningPrognosticsWiener processArtificial neural networkSupervised learningFeature extractionDeep learningPattern recognition (psychology)AccelerationConvolutional neural networkAutoregressive modelRandom forestKernel density estimationFault detection and isolationMoment (physics)Kernel (algebra)Condition monitoringReliability engineeringWeightingMachine Fault Diagnosis TechniquesReliability and Maintenance OptimizationFault Detection and Control Systems