As continuous industrial processes often operate around a desirable region of profitability, the measurement series for most process variables act as stationary series. However, there are inevitably some observed time series which are nonstationary caused by unexpected disturbances. Some series grow slowly for a long time with the equipment aging, and others appear to wander around as if they have no fixed population mean. For these series, traditional dynamic PCA or other statistical modeling methods are not applicable because the statistical properties of variables are time variant. In this paper, nonstationarity test is adopted to distinguish nonstationary series from stationary series. After that, cointegration analysis is used to describe the stochastic common trends and equilibrium error, which can be used to construct monitoring indices. Case study on Tennessee Eastman process shows that the proposed nonstationary process monitoring can efficiently detect faults in the nonstationary dynamic process.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes|
|Publication status||Published - Aug 2014|
|Event||19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - , South Africa|
Duration: 24 Aug 2014 → 29 Aug 2014
Bibliographical noteISBN: 9783902823625 <br/>This work was supported by the members of Texas-Wisconsin-California Control Consortium and the IAPI Fundamental Research Funds (2013ZCX02-01).
- Cointegration analysis
- Dynamic processes
- Nonstationarity test
- Nonstationary multivariate series
- Unit root test