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

Francesco Cannarile (1) , Michele Compare (1) , Piero Baraldi (1) , Francesco Di Maio (1) , Enrico Zio (2, 3)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 LGI - Laboratoire Génie Industriel - EA 2606

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.

Mots clés: 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)

Domaines: Applications [stat.AP]

Francesco Cannarile, Michele Compare, Piero Baraldi, Francesco Di Maio, Enrico Zio. Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components. Machines, 2018, 6 (3), pp.34. ⟨10.3390/machines6030034⟩. ⟨hal-01988959⟩