Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components
| العنوان: | Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components |
|---|---|
| المؤلفون: | Piero Baraldi, Francesco Cannarile, Michele Compare, Francesco Di Maio, Enrico Zio |
| المساهمون: | Dipartimento di Energia [Milano] (DENG), Politecnico di Milano [Milan] (POLIMI), Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec (SSEC), Ecole Centrale Paris-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), Laboratoire Génie Industriel - EA 2606 (LGI), CentraleSupélec |
| المصدر: | Machines Volume 6 Issue 3 Machines, Vol 6, Iss 3, p 34 (2018) Machines, MDPI, 2018, 6 (3), pp.34. ⟨10.3390/machines6030034⟩ |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Work (thermodynamics), Control and Optimization, Computer science, lcsh:Mechanical engineering and machinery, Feature extraction, 0211 other engineering and technologies, Automotive industry, maximum likelihood estimation (MLE), Feature selection, 02 engineering and technology, Markov model, Industrial and Manufacturing Engineering, 020901 industrial engineering & automation, feature selection, Fault Diagnostics, Hidden Semi Markov Model, Computer Science (miscellaneous), lcsh:TJ1-1570, Electrical and Electronic Engineering, Fault Diagnostics, [STAT.AP]Statistics [stat]/Applications [stat.AP], differential evolution (DE), 021103 operations research, business.industry, Mechanical Engineering, feature extraction, homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM), hybrid diagnostic system, Hidden Semi Markov Model, Control and Systems Engineering, Homogeneous, k-nearest neighbors (KNN) classifier, State (computer science), Hidden semi-Markov model, business, Algorithm |
| الوصف: | International audience; 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2075-1702 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85a69ff1d23b9e7d45c007823aebcd42 http://hdl.handle.net/11311/1058758 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....85a69ff1d23b9e7d45c007823aebcd42 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20751702 |
|---|