Myeongsu Kanga, Jaeyoung Kimb, In-Kyu Jeongc, Jong-Myon Kimd, Michael Pechte
aUniversity of Ulsan, Ulsan, South Korea
bSchool of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, South Korea
cSchool of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, South Korea
dDepartment of IT Convergence, University of Ulsan, Ulsan, South Korea
eCenter for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, USA
The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic
frequencies calls for sub-band analysis to determine an
optimal sub-band signal that contains intrinsic information
about bearing faults. In this regard, it is significant to
accurately assess the presence of a bearing's abnormal
symptoms. Hence, a bearing abnormality index (BAI) that
properly quantifies how much information a sub-band
signal contains about bearing faults is presented. Additionally,
to facilitate real-time sub-band analysis based on
the BAI, a massively parallel approach is introduced, where
the approach involves the use of the multicore system.
Likewise, the multicore system supports high-performance
computing by exploiting 128 processing elements operating
at 200 MHz in a Xilinx Virtex-7 field-programmable gate
array (FPGA) device.
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