Title of article :
Analysis and Interpretation of Bearing Vibration Data Using Principal Component Analysis and Self-Organizing Map
Author/Authors :
Patel Raj Kumar نويسنده Department of Electrical Engineering - Madan Mohan Malaviya University of Technology , Giri V. K. نويسنده Department of Electrical Engineering - Madan Mohan Malaviya University of Technology
Pages :
7
From page :
111
To page :
117
Abstract :
Induction motor bearing is one of the key parts of the machine and its analysis and interpretation are important for fault detection. In the present work vibration signal has been taken for the classification i.e. bearing is Healthy (H) or Defective (D). For this purpose, clustering based classification of bearing vibration data has been carried out using Principal Component Analysis (PCA) and Self Organising Map (SOM). From the vibration signal, twelve statistical features have been extracted from both the healthy and the defected condition of the bearing. Further, these data are subjected to PCA to extract significant features relevant to cluster structure. It is observed that out of twelve features only four features are found significant which is feed to the SOM model. The SOM based classification is able to achieve an accuracy of 100%. This cluster-based method of feature reduction and classification could be useful in assessing the induction motor incipient bearing fault detection with large data set.
Journal title :
Astroparticle Physics
Serial Year :
2016
Record number :
2412589
Link To Document :
بازگشت