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Open AccessFeature PaperArticle

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

1
Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
2
Aramis Srl, 20121 Milano, Italy
3
Systems Science and Energetic Challenge, European Foundation for New Energy-Electricitè de France, Ecole Centrale Paris and Superlec, 91190 Paris, France
4
Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Seoul 02447, Korea
*
Author to whom correspondence should be addressed.
Machines 2018, 6(3), 34; https://doi.org/10.3390/machines6030034
Received: 15 June 2018 / Revised: 18 July 2018 / Accepted: 22 July 2018 / Published: 1 August 2018
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-Text
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) 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)
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Cannarile, F.; Compare, M.; Baraldi, P.; Di Maio, F.; Zio, E. Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components. Machines 2018, 6, 34.

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