Adaptive Support Vector Regression for Long-Term Prediction under Nonstationay Environments: Prognostics of Components in Nuclear Power Plants - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Adaptive Support Vector Regression for Long-Term Prediction under Nonstationay Environments: Prognostics of Components in Nuclear Power Plants

Résumé

Prognostics and Health Management (PHM) aims at diagnosing the state and predicting the future evolution of Systems, Structures and Components (SSCs). In real industrial applications, SSCs are normally operated in nonstationary environments and evolving conditions. In our previous work, an adaptive online learning approach is proposed for the short-term (one-day ahead) prediction by Support Vector Regression (SVR), giving satisfactory performance under nonstationary environments. In this paper, this work is extended to long-term (one/two/three/four-weeks ahead) prediction. An application is presented, concerning of the leakage of the first seal of a Reactor Coolant Pump (RCP) of a Nuclear Power Plant (NPP). The results show the adaptability of the proposed approach and offer some reflections on the long-term prediction problem by data-driven approaches.
Fichier principal
Vignette du fichier
adaptive SVR for long-term prediction.pdf (341.69 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01176337 , version 1 (15-07-2015)

Identifiants

  • HAL Id : hal-01176337 , version 1

Citer

Jie Liu, Enrico Zio. Adaptive Support Vector Regression for Long-Term Prediction under Nonstationay Environments: Prognostics of Components in Nuclear Power Plants. 2015 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2015), Jul 2015, Beijing, China. ⟨hal-01176337⟩
613 Consultations
126 Téléchargements

Partager

Gmail Facebook X LinkedIn More