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Proceedings Paper

Fault diagnostics of rotating machines via self-organization
Author(s): Pasi Koikkalainen; Jukka Heikkonen; Tomi Honkanen; Erkki Hakkinen; Jari Mononen
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Paper Abstract

Fault diagnostics of rotating machines requires the concept of novelty. For a set of similar new machines, coming form the assembly line, the typical features of vibration differ from one machine to another. Consequently, one must make a specific model for every machine and test if new, possibly harmful, vibrations will occur during the use of the machine. The classification system must discriminate between familiar and unfamiliar patterns with inclination to reject unseen patterns rather than accept badly distorted familiar ones. In this paper we define the problem and present a solution, based on a self-organizing map. It allows us to cluster different normal runtime characteristics of machines and classify new measurements. Detection of novelty is made by examining the difference between class features of old and new observations.

Paper Details

Date Published: 29 October 1996
PDF: 9 pages
Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); doi: 10.1117/12.256303
Show Author Affiliations
Pasi Koikkalainen, Jyvaskyla Univ. (Finland)
Jukka Heikkonen, Lappeenranta Univ. of Technology (Finland)
Tomi Honkanen, Lappeenranta Univ. of Technology (Finland)
Erkki Hakkinen, Lappeenranta Univ. of Technology (Finland)
Jari Mononen, Lappeenranta Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 2904:
Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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