Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components

Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which a...

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Bibliographic Details
Main Authors:	Francesco Cannarile, Michele Compare, Piero Baraldi, Francesco Di Maio, Enrico Zio
Format:	Article
Language:	English
Published:	MDPI AG 2018-08-01
Series:	Machines
Subjects:	hybrid diagnostic system feature extraction feature selection k-nearest neighbors (KNN) classifier homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM) maximum likelihood estimation (MLE) differential evolution (DE)
Online Access:	http://www.mdpi.com/2075-1702/6/3/34

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