Open Access
Open access
MDPI
Machines
,  volume 6 ,  issue 3 ,  pages 34

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

Publication type: Journal Article
Publication date: 2018-08-01
MDPI
scimago Q2
wos Q2
SJR: 0.570
CiteScore: 4.7
Impact factor: 2.5
ISSN: 20751702
Electrical and Electronic Engineering
Computer Science (miscellaneous)
Mechanical Engineering
Industrial and Manufacturing Engineering
Control and Systems Engineering
Control and Optimization
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.

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GOST |
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GOST Copy
Cannarile F. et al. Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components // Machines. 2018. Vol. 6. No. 3. p. 34.
GOST all authors (up to 50) Copy
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. Vol. 6. No. 3. p. 34.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.3390/machines6030034
UR - https://www.mdpi.com/2075-1702/6/3/34
TI - Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components
T2 - Machines
AU - Cannarile, Francesco
AU - Compare, Michele
AU - Baraldi, Piero
AU - Di Maio, Francesco
AU - Zio, Enrico
PY - 2018
DA - 2018/08/01
PB - MDPI
SP - 34
IS - 3
VL - 6
SN - 2075-1702
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2018_Cannarile,
author = {Francesco Cannarile and Michele Compare and Piero Baraldi and Francesco Di Maio and Enrico Zio},
title = {Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components},
journal = {Machines},
year = {2018},
volume = {6},
publisher = {MDPI},
month = {aug},
url = {https://www.mdpi.com/2075-1702/6/3/34},
number = {3},
pages = {34},
doi = {10.3390/machines6030034}
}
MLA
Cite this
MLA Copy
Cannarile, Francesco, et al. “Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components.” Machines, vol. 6, no. 3, Aug. 2018, p. 34. https://www.mdpi.com/2075-1702/6/3/34.