Fault diagnostics of rotating machines via self-organization

被引:1
|
作者
Koikkalainen, P
Heikkonen, J
Honkanen, T
Hakkinen, E
Mononen, J
机构
关键词
neural networks; fault diagnostics; rotating machines; self-organization; SOM;
D O I
10.1117/12.256303
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fault diagnostics of rotating machines requires the concept of novelty. For a set of similar new machines, coming from 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.
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页码:460 / 468
页数:9
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