Estimation and forecasting of machine health condition using ARMA/GARCH model |
| |
Authors: | Hong Thom Pham Bo-Suk Yang |
| |
Affiliation: | aSchool of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea |
| |
Abstract: | This paper proposes the hybrid model of autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH) to estimate and forecast the machine state based on vibration signal. The main idea in this study is to employ the linear ARMA model and the nonlinear GARCH model to explain the wear and fault condition of machine, respectively. The successful outcomes of the ARMA/GARCH prediction model can give obvious explanation for future states of machine, which enhance the worth of machine condition monitoring as well as condition-based maintenance in practical applications. The advance of the proposed model is verified in empirical results as applying for a real system of a methane compressor in a petrochemical plant. |
| |
Keywords: | Generalized autoregressive conditional heteroscedasticity (GARCH) Autoregressive moving average (ARMA) Machine fault prediction |
本文献已被 ScienceDirect 等数据库收录! |