Archives of Acoustics, 37, 2, pp. 131–141, 2012

Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution

AGH University of Science and Technology

Mechanical Engineering Department, Prince Mohammad Bin Fahd University (PMU)

Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition mon-
itoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an
excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component
of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application
to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its
combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of
the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a
wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement
for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling
element bearings through providing an insight into the usage of MED in rolling element bearings diag-
nostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates
these on new interesting cases both from a lab environment and an actual case.
Keywords: rolling bearing; fault detection; Minimum Entropy Deconvolution (MED); wind turbine
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