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

Internet

http://www.mdpi.com/2075-1702/6/3/34

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