A strategy for detection and isolation of sensor failures and process upsets
| Autor: | Ahmet Palazoglu, Fuat Doymaz, Jose A. Romagnoli |
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| Rok vydání: | 2001 |
| Předmět: |
Correlation coefficient
Computer science Calibration (statistics) Process Chemistry and Technology Real-time computing Process (computing) Root cause Fault detection and isolation Computer Science Applications Analytical Chemistry Principal component analysis Benchmark (computing) Isolation (database systems) Spectroscopy Software |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 55:109-123 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/s0169-7439(00)00126-x |
| Popis: | A novel approach is proposed to isolate sensors that are affected by the root cause of nonconforming operation and to distinguish between failed sensors and process upsets. Systems having multivariate nature can be monitored by building a principal component analysis (PCA) model using historical data. T 2 and sum-of-squared-prediction error (SPE) of the calibration model facilitate fault detection and isolation on-line. These two measures are complementary in explaining the events captured and not captured by the model. In this paper, we put more emphasis on the importance of using the T 2 and the SPE together for fault detection and identification. Correlation coefficient criterion was utilized to infer about the state of the correlation structure between one sensor and its closest neighbor for distinguishing between sensor failures and process upsets. Faulty measurements were reconstructed from available sensors using the calibration model and an optimization algorithm which in turn unveiled more process upsets. The strategy is illustrated on a benchmark industrial liquid-fed ceramic melter. |
| Databáze: | OpenAIRE |
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