ISSN:
1573-2754
Keywords:
pattern recognition
;
condense state information
;
divergence index
;
inter-object distance
;
intra-object distance
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
,
Mathematics
,
Physics
Notes:
Abstract In this paper, the characteristics of vibration signal of machinery in different running conditions are statistically analysed, and some moments of statistical distribution of signals are selected as the eigenvector to condense the state information. Here, we divide the states of machinery into two: ‘good’ and ‘faulty’, and the pattern recognition techniques are used to classify the running conditions of machinery. At the end of this paper, the authors present some test data, and from the results obtained, it's verified that the eigenvector selected is reliable and sensible to faults. And the results also show the effectiveness of classification rule.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02458164
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