A Novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser Energy Operator

Ali Tabrizi, Luigi Garibaldi, Alessandro Fasana, and Stefano Marchesiello
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Uncertainty in PHM
Submission Type: 
Full Paper
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ijphm_15_026.pdf876.44 KBOctober 4, 2015 - 12:28pm

Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zer0-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method.

Publication Year: 
2015
Publication Volume: 
6
Publication Control Number: 
026
Page Count: 
10
Submission Keywords: 
anomaly detection
Roller bearing
feature extraction
ensemble empirical mode decomposition (EEMD)
Teager-Kaiser energy operator (TKEO)
One-Class SVM
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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