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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rojas-Guzman, C. Kramer, M.A. |
| Copyright Year | 1994 |
| Description | Author affiliation: Dept. of Chem. Eng., MIT, Cambridge, MA, USA (Rojas-Guzman, C.; Kramer, M.A.) |
| Abstract | Remote diagnosis of industrial and manufacturing facilities constitutes a feasible alternative at high-risk or remote sites where unmanned operation is preferred. Computer-aided diagnostic tools can reduce downtime by providing support to remote monitoring centers and on-site plant operators. This paper describes a novel technique to perform on-line remote monitoring and diagnosis of industrial and manufacturing systems based on Bayesian belief networks and genetic algorithms. An implementation of the methodology in a chemical process industry is presented and potential applications for different types of industrial systems are discussed. |
| Starting Page | 363 |
| Ending Page | 367 |
| File Size | 405464 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780319613 |
| DOI | 10.1109/ISIE.1994.333089 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-05-25 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Remote monitoring Genetic algorithms Bayesian methods Manufacturing industries Chemical industry Inference algorithms Chemical engineering Manufacturing processes Production facilities Fault detection |
| Content Type | Text |
| Resource Type | Article |
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