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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoling Bian Quanzhi Xu Bo Li Limei Xu |
| Copyright Year | 2007 |
| Description | Author affiliation: Univ. of Electron. Sci. & Technol. of China, Chengdu (Xiaoling Bian; Quanzhi Xu) |
| Abstract | 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. |
| Starting Page | 2095 |
| Ending Page | 2099 |
| File Size | 186099 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424415311 |
| DOI | 10.1109/ICAL.2007.4338921 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | ARMA model Time series analysis Finance forecasting Predictive models Predictive maintenance Manufacturing processes Neural networks Electronic equipment manufacture Production Technology forecasting data transformation Autoregressive processes |
| Content Type | Text |
| Resource Type | Article |
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