Equipment Fault Forecasting Based on a Two-level Hierarchical Model
The analysis of historical time series data that reflects equipment failures is becoming increasingly important in maintenance policies in manufacturing plant. In this paper, we propose a two-level hierarchical modeling framework whose higher level is a model for trend prediction, while whose lower level is a model for residual prediction. Solving the lower level problem is the main focus of this paper. Auto-regressive moving average (ARMA) model is used for residual prediction. One data transformation method is adopted to obtain mean stationary time series by using a defined historical data, which is calculated by an algorithm. The ARMA model which is extensively used in trend and future behavior prediction is used to provide a rigorous prediction of the residual series extracted in the data transformation method. By combining trend prediction and residual prediction approaches, the proposed method can effectively handle the non-linear situation with equipment of highly complicated and non-stationary nature. Its effectiveness has been illustrated by an analysis of real-world data. The proposed method is helpful to reflect the equipment condition and thereby can aid predictive maintenance in manufacturing and reduce the downtime.
ARMA model data transformation forecasting
Xiaoling Bian Quanzhi Xu Bo Li Limei Xu
School of Applied Mathematics University of Electronic Science and Technology of China Chengdu 61005 Institute of Astronautics & Aeronautics University of Electronic Science and Technology of China Che
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）