Abstract
This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network architectures evaluated are the multi-layer feed-forward network and the recurrent network. Simulation results on a set of compressor failures showed that in modeling the stochastic nature of reliability data, both the ARIMA and the recurrent neural network (RNN) models outperform the feed-forward model; in terms of lower predictive errors and higher percentage of correct reversal detection. However, both models perform better with short term forecasting. The effect of varying the damped feedback weights in the recurrent net is also investigated and it was found that RNN at the optimal weighting factor gives satisfactory performances compared to the ARIMA model.
Original language | English |
---|---|
Pages (from-to) | 371-375 |
Number of pages | 5 |
Journal | Computers and Industrial Engineering |
Volume | 42 |
Issue number | 2-4 |
DOIs | |
Publication status | Published - 11 Apr 2002 |
Externally published | Yes |
Keywords
- Box-Jenkins autoregressive integrated moving average model
- Multi-layer feed-forward neural network
- Recurrent neural network
ASJC Scopus subject areas
- Management Science and Operations Research
- Information Systems and Management
- Industrial and Manufacturing Engineering
- Applied Mathematics