%0 Journal Article
%T Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components
%A Enrico Zio
%A Francesco Cannarile
%A Francesco Di Maio
%A Michele Compare
%A Piero Baraldi
%J -
%D 2018
%R https://doi.org/10.3390/machines6030034
%X Abstract 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 adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS. View Full-Tex
%K hybrid diagnostic system
%K feature extraction
%K feature selection
%K k-nearest neighbors (KNN) classifier
%K homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM)
%K maximum likelihood estimation (MLE)
%K differential evolution (DE)
%U https://www.mdpi.com/2075-1702/6/3/34