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Abstract: For the knowledge acquisition from the data table recorded
faults cases with continuous-value features, the discretization-mapping
scheme translating the table into the generalization information system
of rough set theory is investigated. The direct k-means algorithm of the
clustering analysis is improved. The amount of faults confirmed by
experts in the table is set as the classification amount k and the set
of indexes of mean clustering centers ordered by sort ascending is set
as mapping function. On the basis of the partition universe with k, the
discretization scheme to the table is proposed. The discrete symbol
denoting a continuous value is the same as the symbol of the center
provided with the nearest distance between the value and a group of
clustering centers. By the process, a normal table accord with the
pattern of rough set theory is acquired and a few decision-making rules
are extracted. The rules show that the scheme has the performances
optimizing the partition points and rejecting the outside fluctuation.
They reveal the generalization characters of the faults and can be used
to construct and to extend the knowledge database of the fault diagnosis
devices of a rotor-bearings system.
Key words: Fault diagnosis Rough set Cluster analysis Attributes
discretization Knowledge acquisition
CLC No: TP274
TH165.3
国家自然科学基金资助项目(50335030).
Received 20040419, received in revised form 20041020
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