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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Głuch, Jerzy Krzyz˙anowski, Jerzy |
| Copyright Year | 2002 |
| Abstract | Thermal and flow diagnostics of power units makes use of diagnostic relations i.e. relations between fault signatures (sets of symptoms) and geometry degradation of its components. Determining symptoms may base on thorough thermal measurements of the cycle. However, numerous apparatuses in the cycle are not or cannot be properly equipped for necessary measurements. Examples of such apparatuses in a steam turbine are external glands and nozzle box sealings. The paper studies the applicability of a selected type of Artificial Neural Network, ANN, as a diagnostic relation for locating faulty apparatuses in HP and IP turbine casings, including their sealing systems. The obtained results can be assessed as good for single faults, and satisfactory for multiple faults of the cycle components. The examined type of ANN can be used e.g. in a modular hierarchical diagnostic system proposed by Gluch & Krzyzanowski, 1998, 1999. |
| Sponsorship | International Gas Turbine Institute |
| Starting Page | 83 |
| Ending Page | 91 |
| Page Count | 9 |
| File Format | |
| ISBN | 079183607X |
| DOI | 10.1115/GT2002-30028 |
| e-ISBN | 0791836010 |
| Volume Number | Volume 2: Turbo Expo 2002, Parts A and B |
| Conference Proceedings | ASME Turbo Expo 2002: Power for Land, Sea, and Air |
| Language | English |
| Publisher Date | 2002-06-03 |
| Publisher Place | Amsterdam, The Netherlands |
| Access Restriction | Subscribed |
| Subject Keyword | Geometry Cycles Artificial neural networks Thermodynamic power cycles Sealing (process) Nozzles Steam turbines Flow (dynamics) Turbines |
| Content Type | Text |
| Resource Type | Article |
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