Details
| Original language | English |
|---|---|
| Pages (from-to) | 161-176 |
| Number of pages | 16 |
| Journal | Analytica chimica acta |
| Volume | 313 |
| Issue number | 3 |
| Publication status | Published - 29 Sept 1995 |
Abstract
Effective automated supervision can help to ensure the reliable operation of complex flow-injection analysis (FIA) systems. As an important element of a supervisory system, fast fault detection of the FIA systems is required. In this paper, a model-based fault detection method based on the identification of the model parameters is developed. The fault detection system consists of the three levels estimation, filtering and evaluation of the model parameters. The model order and the time delay of the system are determined on-line. The recursive fixed memory (RFM) method is used to estimate model parameters. The fault detection of a FIA system is performed by means of filtering the estimated model parameters through a high- and low-pass filter for separation of faults with different magnitude in their dynamics. The application to different practical examples confirms that the newly developed method offers a very effective way for fault detection in the FIA process.
Keywords
- Digital filtering, Flow injection, On-line identification, Recursive least squares
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Environmental Science(all)
- Environmental Chemistry
- Chemistry(all)
- Spectroscopy
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In: Analytica chimica acta, Vol. 313, No. 3, 29.09.1995, p. 161-176.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - On-line fault detection of flow-injection analysis systems based on recursive parameter estimation
AU - Wu, Xiaoan
AU - Bellgardt, Karl Heinz
PY - 1995/9/29
Y1 - 1995/9/29
N2 - Effective automated supervision can help to ensure the reliable operation of complex flow-injection analysis (FIA) systems. As an important element of a supervisory system, fast fault detection of the FIA systems is required. In this paper, a model-based fault detection method based on the identification of the model parameters is developed. The fault detection system consists of the three levels estimation, filtering and evaluation of the model parameters. The model order and the time delay of the system are determined on-line. The recursive fixed memory (RFM) method is used to estimate model parameters. The fault detection of a FIA system is performed by means of filtering the estimated model parameters through a high- and low-pass filter for separation of faults with different magnitude in their dynamics. The application to different practical examples confirms that the newly developed method offers a very effective way for fault detection in the FIA process.
AB - Effective automated supervision can help to ensure the reliable operation of complex flow-injection analysis (FIA) systems. As an important element of a supervisory system, fast fault detection of the FIA systems is required. In this paper, a model-based fault detection method based on the identification of the model parameters is developed. The fault detection system consists of the three levels estimation, filtering and evaluation of the model parameters. The model order and the time delay of the system are determined on-line. The recursive fixed memory (RFM) method is used to estimate model parameters. The fault detection of a FIA system is performed by means of filtering the estimated model parameters through a high- and low-pass filter for separation of faults with different magnitude in their dynamics. The application to different practical examples confirms that the newly developed method offers a very effective way for fault detection in the FIA process.
KW - Digital filtering
KW - Flow injection
KW - On-line identification
KW - Recursive least squares
UR - http://www.scopus.com/inward/record.url?scp=0029071287&partnerID=8YFLogxK
U2 - 10.1016/0003-2670(95)00236-S
DO - 10.1016/0003-2670(95)00236-S
M3 - Article
AN - SCOPUS:0029071287
VL - 313
SP - 161
EP - 176
JO - Analytica chimica acta
JF - Analytica chimica acta
SN - 0003-2670
IS - 3
ER -