Damage identification and external effects removal for roller bearing diagnostics

Miriam Pirra, Alessandro Fasana, Luigi Garibaldi, and Stefano Marchesiello
Submission Type: 
Full Paper
phmce_12_039.pdf363.36 KBJune 4, 2012 - 3:42pm

In this paper we introduce a method to identify if a bearing is
damaged by removing the effects of speed and load. In fact,
such conditions influence vibration data during acquisitions
in rotating machinery and may lead to biased results when di-
agnostic techniques are applied. This method combines Em-
pirical Mode Decomposition (EMD) and Support Vector Ma-
chine classification method. The vibration signal acquired is
decomposed into a finite number of Intrinsic Mode Functions
(IMFs) and their energy is evaluated. These features are then
used to train a particular type of SVM, namely One-Class
Support Vector Machine (OCSVM), where only one class of
data is known. Data acquisition is done both for a healthy
bearing and for one whose rolling element presents a 450 μm
damage. We consider three speeds and three different radial
loads for both bearings, so nine conditions are acquired for
each type of bearing overall. Feature evaluation is done using
EMD and then healthy data belonging to the various condi-
tions are taken into account to train the OCSVM. The remain-
ing data are analysed by the classifier as test object. The real
class each element belongs to is known, so the efficiency of
the method cab be measured by counting the errors made by
the labelling procedure. These evaluations are performed by
applying different kinds of SVM kernel.

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Publication Volume: 
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Submission Keywords: 
Empirical Mode Decomposition
One-Class SVM
speed and load effect removal
bearing diagnostics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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