a smart monitoring system for bearing fault detection
abstract
rolling element bearings are commonly used in rotating machinery to support shafts,
reduce friction, and increase power transmission efficiency. for a machinery system, bearing
fault could be the most possible cause of mechanical failures. if bearing defect can be
detected at its early stage, mechanical performance degradation and even economic losses
can be avoided. although many signal processing techniques have been proposed in the
literature for bearing fault detection, reliable bearing fault diagnosis is still a challenging task
in this r&d field, especially in industrial applications. the objective of this work is to
develop a smart condition monitoring system and a signal processing technique for bearing
fault detection. firstly, a field programmable gate arrays (fpga) based sinusoidal generator
is developed to generate controllable sinusoidal waveforms and explore fpga’s potential
applications in a data acquisition system to collect vibration signals. secondly, an adaptive
variational mode decomposition (avmd) technique is proposed for bearing fault detection.
the avmd includes several steps in processing: 1) signal characteristics are analyzed to
determine the signal center frequency and the related parameters. 2) the ensemble-kurtosis
index is suggested to select the optimal intrinsic mode function (imf) to decompose the
target signal. 3) the envelope spectrum analysis is performed using the selected imf to
identify the representative features for bearing fault detection. the effectiveness of the
proposed avmd technique is examined by simulation and experimental tests under different
bearing conditions, with the comparison of other related bearing fault techniques.