scholarly journals Fault diagnosis of rolling element bearing using Naïve Bayes classifier

2017 ◽  
Vol 14 ◽  
pp. 64-69
Author(s):  
Peng Hou ◽  
Xiao Jian Yi ◽  
Yue Feng Chen
Keyword(s):  
Fault Diagnosis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Rolling Element Bearing ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Rolling Element ◽  
Element Bearing

scholarly journals Diagnosa Kerusakan Bearing Menggunakan Principal Component Analysis (PCA) dan Naïve Bayes Classifier

2016 ◽  
Vol 6 (2) ◽  
pp. 114
Author(s):  
Dwi Pudyastuti ◽  
Toni Prahasto ◽  
Achmad Widodo
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Naive Bayes ◽  
Principal Component ◽  
Component Analysis ◽  
Naïve Bayes ◽  
Feature Reduction ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

This research is discussing about the usage of data mining which addressed for bearing fault diagnosis. Bearing was one of the essential parts in industry machinery. Bearing was used to reduce machines frictions or could be a moving component which oppressed each other.  This fault diagnosis can avoid loss and damage of other machines components. This research was started with data preprocessing using wavelet discrete transformation, feature extraction, feature reduction using Principal Component Analysis (PCA), and classification process using Naïve Bayes classifier methods. Naïve Bayes Classifier is a classification method which based on probability and Bayesian theorem. Output of these method shows that Naïve Bayes classification have a good performance which shown by a good accuracy in each data test.

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