| DOI | Trouver le DOI : https://doi.org/10.1007/s10489-017-1083-0 |
|---|
| Auteur | Rechercher : Yang, Chunsheng1; Rechercher : Lou, Qingfeng; Rechercher : Liu, Jie; Rechercher : Yang, Yubin; Rechercher : Cheng, Qiangqiang |
|---|
| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
|
|---|
| Format | Texte, Article |
|---|
| Sujet | machine learning; predictive maintenance; particle filtering; Time to Failure (TTF); modeling |
|---|
| Résumé | One of core technologies for prognostics is to predict failures before they occur and estimate time to failure (TTF) by using built-in predictive models. The predictive model could be either physics-based model or machine learning-based model. Machine learning-based predictive modeling is an emerging application of machine learning to machinery maintenance. Accurate TTF estimation could help performing predictive action “just-in-time”. However, the developed predictive models sometimes fail to provide a precise TTF estimate. To address this issue, we propose a Particle Filtering (PF)-based method to estimate TTF. After introducing the PF-based algorithm, we present the implementation along with the experimental results obtained from a case study of Auxiliary Power Unit (APU) prognostics. To our best knowledge, this is the first application of PF-based method to APU prognostic. The results demonstrated that the PF-based method is useful for estimating TTF for predictive maintenance and it greatly improved TTF estimation precision for APU prognostics. |
|---|
| Date de publication | 2017-12-06 |
|---|
| Maison d’édition | Springer |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 8f99be20-8858-469e-ad63-f934c772b105 |
|---|
| Enregistrement créé | 2019-04-26 |
|---|
| Enregistrement modifié | 2020-03-16 |
|---|
