Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models

farughi, hiwa; hakimi, ahmad; kamranrad, reza

doi:10.22068/ijiepr.29.1.79

Volume 29, Issue 1 (IJIEPR 2018) IJIEPR 2018, 29(1): 79-90 | Back to browse issues page

‎ 10.22068/ijiepr.29.1.79

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farughi H, hakimi A, kamranrad R. Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models. IJIEPR 2018; 29 (1) :79-90
URL: http://ijiepr.iust.ac.ir/article-1-778-en.html

Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models

Hiwa Farughi

¹, Ahmad Hakimi²

, Reza Kamranrad³

1- University of Kurdistan , h.farughi@uok.ac.ir
2- University of Kurdistan
3- University of Science and Culture

Abstract: (4796 Views)

In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods.

Keywords: Availability, prediction, artificial neural network, exponentially weighted moving average, time series model.

Full-Text [PDF 461 kb] (1737 Downloads)

Type of Study: Research | Subject: Reliability and Maintenance
Received: 2017/08/5 | Accepted: 2017/12/26 | Published: 2018/02/21