Fault diagnostics of rotating machines via self-organization

Pasi Koikkalainen; Jukka Heikkonen; Tomi Honkanen; Erkki Hakkinen; Jari Mononen

doi:10.1117/12.256303

29 October 1996 Fault diagnostics of rotating machines via self-organization

Pasi Koikkalainen, Jukka Heikkonen, Tomi Honkanen, Erkki Hakkinen, Jari Mononen

Proceedings Volume 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling; (1996) https://doi.org/10.1117/12.256303
Event: Photonics East '96, 1996, Boston, MA, United States

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.

Citation Download Citation

Pasi Koikkalainen, Jukka Heikkonen, Tomi Honkanen, Erkki Hakkinen, and Jari Mononen "Fault diagnostics of rotating machines via self-organization", Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996); https://doi.org/10.1117/12.256303

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