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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Upadhyaya, B. R. Mathai, G. Green, J. D. |
| Copyright Year | 1988 |
| Description | Author affiliation: The University of Tennessee, Knoxville (Upadhyaya, B. R.; Mathai, G.) || Alcoa Technical Center, Pittsburgh (Green, J. D.) |
| Abstract | The characterization of a data cluster representing a certain process behavior is achieved by developing steady-state nonlinear modeling of one or more critical signals as a function of other process variables in the system. This prediction model is used to detect either sensor maloperation or process anomaly by comparing the prediction and measurement of the same variable. A large database from a process control system can be grouped using clustering algorithms. Automated generation of prediction models are applied to an industrial process to study the performance of this database management approach. |
| Starting Page | 650 |
| Ending Page | 651 |
| File Size | 155526 |
| Page Count | 2 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1988-06-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Fault detection Clustering algorithms Predictive models Databases Signal processing Monitoring Instruments Polynomials Milling machines Steady-state |
| Content Type | Text |
| Resource Type | Article |
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