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Abstract: A
new method, distance mapping, is presented in order to visualize the
trained results by self-organizing maps (SOM) apparently. By means of
similarities evaluated based on Euclidean distances between input
vectors and output neurons weights combining with the distribution of
fixed lattices in the network, high-dimensional input vectors are
projected into a two-dimensional space. SOM is employed in fault
recognition and condition monitoring of gearbox combining with the
proposed visualizing technique. It is proved that feature points under
gear normal, tooth cracked and tooth broken conditions are mapped into
different areas on two-dimensional space more clearly by distance
mapping than U-matrix method, which helps distinguish gearbox conditions
correctly. With the trace of the image points for gear feature data on
the plane, the variation of gearbox conditions is observed visually, and
furthermore, early gear failures occurrence and its varying trend is
monitored in time.
Key words: Self-organizing maps Visualization U-matrix Distance
mapping Condition monitoring
CLC No: TH17 TP1.8
国家自然科学基金(50205009)和湖北省自然科学基金(2000J125)资助项目. Received 20020814, received
in revised form 20030630
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