Decomposition of Symptom Observation Matrix and its Optimization in Vibration Condition Monitoring of Machines |
| Journal |
Applied Mechanics and Materials (Volume 9) |
| Volume |
New Trends in Mechanics and Transport |
| Edited by |
Tadeusz Uhl and Andrzej Chudzikiewicz |
| Pages |
51-60 |
| DOI |
10.4028/www.scientific.net/AMM.9.51 |
| Online since |
October, 2007 |
| Authors |
Czesław Cempel
|
| Keywords |
Fault Space, Multidimensional Observation, Observation Space, Singular Value Decomposition (SVD), Vibration |
| Abstract |
With the modern metrology we can measure almost all variables in the phenomenon field
of a working machine, and many of measuring quantities can be symptoms of machine condition.
On this basis we can form the symptom observation matrix (SOM) intended for condition
monitoring. On the other hand we know, that contemporary complex machines may have many
modes of failure, so called faults. The paper presents a method for the extraction of fault
information from the symptom observation matrix by means of singular value decomposition
(SVD) in the form of generalized fault symptoms. As the readings of the symptoms can be unstable,
the moving average of the SOM was applied with success. The attempt to assess the diagnostic
contribution of primary symptom was undertaken, and also some approach to connect SVD
methodology with neural nets is considered. These possibilities are illustrated in the paper by
processing data taken directly from the vibration condition monitoring of the machine. |
| Full Paper |
Get the full paper by clicking here
|
| Preview |
Free first page example |
