Application of multi-step time series prediction for industrial equipment prognostic

Autor:	Rosmiza Wahida Abdullah, Mohd Norhisham Che Soh, Burairah Hussin, Abd Samad Hasan Basari, Siti Azirah Asmai
Rok vydání:	2011
Předmět:	Industrial equipment Artificial neural network Series (mathematics) Computer science business.industry Condition monitoring Control engineering Machine learning computer.software_genre Maintenance engineering Sliding window protocol Prognostics Artificial intelligence Time series business computer
Zdroj:	2011 IEEE Conference on Open Systems.
DOI:	10.1109/icos.2011.6079285
Popis:	The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of industrial equipment.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c248d1c4283c8d7683b52c2044f01446 https://doi.org/10.1109/icos.2011.6079285 Zobrazit plný text záznamu