Jing Tian, Carlos Morillo, Michael H. Azarian, Member, IEEE, and Michael Pecht, Fellow, IEEE
b CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, USA
Bearing faults are the main contributors to the
failure of electric motors. Although a number of vibration
analysis methods have been developed for the detection of
bearing faults, false alarms still result in losses. This paper
presents a method that detects bearing faults and monitors
the degradation of bearings in electric motors. Based on
spectral kurtosis (SK) and cross correlation, the method
extracts fault features that represent different faults, and
the features are then combined to form a health index
using principal component analysis (PCA) and a semisupervised
k-nearest neighbor (KNN) distance measure. The
method was validated by experiments using a machinery
fault simulator and a computer cooling fan motor bearing.
The method is able to detect incipient faults and diagnose
the locations of faults under masking noise. It also provides
a health index that tracks the degradation of faults without
missing intermittent faults. Moreover, faulty reference data
are not required.
This article is available online here and to CALCE Consortium Members for personal review.