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

• Main Authors: Francesco Cannarile, Michele Compare, Piero Baraldi, Francesco Di Maio, Enrico Zio
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: Machines
• Subjects: 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)
• Online Access: http://www.mdpi.com/2075-1702/6/3/34

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.