Title:
SMART PARALLEL WAVELET TRANSFORMATIONS FOR EDGE AND FOG DETECTION OF BEARING DEFECTS
SMART PARALLEL WAVELET TRANSFORMATIONS FOR EDGE AND FOG DETECTION OF BEARING DEFECTS
Author(s)
Rauby, Pierrick
Advisor(s)
Kurfess, Thomas R.
Editor(s)
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Abstract
Rolling Element Bearings (REB) are critical components of a wide range of
rotating machines. Identifying and preventing their faults is critical for
safe and efficient equipment operation. A variety of condition monitoring
techniques have been developed that gather large amounts of data using
acoustic or vibration transducers. Further information about the health of an
REB can be extracted via time domain trend analysis, and amplitude modulation
technics. The frequency domain-specific peaks corresponding to the defects can
also be identified directly from the spectrum.
Such approaches either provide
little insight into the type of defect, are sensitive to noise, and require
substantial post-processing. Complicating current fault diagnostic approaches
are the ever-increasing size of datasets from different types of sensors that
yield non-homogeneous databases and more challenging to execute prognostics
for large-scale condition-based maintenance. These difficulties are
addressable via approaches that leverage recent developments on
microprocessors and system on chip (SoC) enabling more processing power at the
sensor level, unloading the cloud from non-used or low information density data.
The proposed research addresses these limitations by presenting a new
approach for bearing defect detection using a SoC network to perform a wavelet
transform calculation. The wavelet transforms enable an improved time-
frequency representation and is less sensitive to noise than other classical
methods; however, its analysis requires more complex processing techniques
that must be executed at the edge (sensor) to limit the need for cloud
computing of the results and large-scale data transmission to the cloud. To
enable near real-time processing of the data, the BeagleBone AI SoC is employed, the wavelet transforms, and the defect classification are achieved at the
edge.
The contributions of this work are as follows: first, the real-time data
acquisition driver for the SoC is developed. Second, the machine learning
algorithm for improving the wavelet transform and the defect identification is
implemented. Third federated learning in a network of SoC is formulated and
implemented. Finally, the new approach is benchmarked to current approaches in
terms of detection accuracy, and sensitivity to defect and was proven to
obtain between 80 and 90 percent accuracy depending on the dataset.
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Date Issued
2021-12-14
Extent
Resource Type
Text
Resource Subtype
Dissertation