Abstract
The paper deals with acquisition of diagnostic knowledge that is relevant for detection and isolation of a special class of malfunctions of rotating machinery called "shaft misalignment". To detect a misalignment of the given shaft supported by multiple journal bearings, decision trees have been applied. These trees have been discovered in a database collected in a numerical experiment performed by the well-verified simulation system. A novel approach to definition of classes of misalignment has been introduced. Several new methods of selection of attributes and evaluation of classifier's performance have been suggested and verified. Finally a new method of diagnosing misalignment of rotating machinery has been formulated. This method may be efficiently implemented for real-existing rotating machinery.
| Original language | English |
|---|---|
| Pages (from-to) | 126-137 |
| Number of pages | 12 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 4730 |
| DOIs | |
| Publication status | Published - 2002 |
| Event | Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV - Orlando, FL, United States Duration: 1 Apr 2002 → 4 Apr 2002 |
Keywords
- Diagnostic knowledge
- Knowledge discovery
- Rotating machinery
- Shaft misalignment
- Static models
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
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