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Machines 2018, 6(3), 34; https://doi.org/10.3390/machines6030034

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.
Received: 15 June 2018 / Revised: 18 July 2018 / Accepted: 22 July 2018 / Published: 1 August 2018
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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-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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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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