Artificial neural network and fuzzy logic based boiler tube leak detection systems
US6192352A · kind A · utility
Assignees
Inventors
Key dates
| Filing date | Feb 20, 1998 |
| Grant date | Feb 20, 2001 |
| Priority date | — |
| Expiry date | Feb 20, 2018 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S706/907
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior. The second design philosophy integrates ANNs with approximate reasoning using fuzzy logic and fuzzy sets. In the second design, ANNs are used for learning, while approximate reasoning and inference engines are used for decision making. Advantages include use of already mon…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.