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

Author

Asmai, Siti Azirah ; Abdullah, Rosmiza Wahida ; Soh, Mohd Norhisham Che ; Basari, Abd Samad Hasan ; Hussin, Burairah

Author_Institution

Fac. of Inf. & Commun. Technol., Univ. Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

fYear

2011

fDate

25-28 Sept. 2011

Firstpage

273

Lastpage

277

Abstract

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.

Keywords

condition monitoring; maintenance engineering; mechanical engineering computing; neural nets; time series; artificial neural network technique; condition monitoring data; industrial equipment prognostics; multistep time series prediction; Autoregressive processes; Biological neural networks; Degradation; Mathematical model; Neurons; Predictive models; Time series analysis; failure propability; multi-step prediction; neural network; prognostic; time series prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Open Systems (ICOS), 2011 IEEE Conference on

Conference_Location

Langkawi

Print_ISBN

978-1-61284-931-7

Type

conf

DOI

10.1109/ICOS.2011.6079285

Filename

6079285