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

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.

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)