1School of Mechanical Engineering, University of Jinan, Jinan 250022, China
2Center for Advanced Life Cycle Engineering, University of Maryland at College Park, College Park, MD 20742, USA
3School of Electrical Engineering, University of Jinan, Jinan 250022, China
4School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Abstract:
High data throughput during real-time vibration monitoring can easily lead to network
congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a
new computing paradigm, edge computing is deemed to be a good solution to these problems. In this
paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data
dimensionality but also to extract and represent the machine condition information. A sub-band coding
method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic
aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method
is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into
a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can
benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic
results can still be achieved. The effectiveness of the developed method is verified with two benchmark
bearing datasets. Considerations on practical condition monitoring applications are also presented.