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- 2018
Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial ComponentsDOI: https://doi.org/10.3390/machines6030034 Keywords: 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) Abstract: 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
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