Compressed Sensing Based on an Improved K-SVD for Vibration Signal Compression Reconstruction in Wireless Sensor Networks

Compressed Sensing Based on an Improved K-SVD for Vibration Signal Compression Reconstruction in Wireless Sensor Networks

Compressed sensing (CS) is an effective method to improve the processing of mechanical vibration signals in prognostics health management (PHM). Aiming to address the severe lack of storage and computational resources and the high delay of transmitting massive vibration data in wireless sensor netwo...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 11
Main Authors: Huang, Qingqing, Li, Zonghua, Han, Yan, Zhang, Yan, Zhao, Chunhua, Cai, Wuxia, Ma, Jinghua
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Compressed sensing (CS)
Compression ratio
compression reconstruction
Dictionaries
k-singular value decomposition algorithm (K-SVD)
Matched pursuit
Matching pursuit algorithms
Optimization
Reconstruction
Singular value decomposition
Sparse approximation
Sparse matrices
sparse representation
Sparsity
Vibration
vibration signal
Vibrations
Wireless sensor networks
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Summary:Compressed sensing (CS) is an effective method to improve the processing of mechanical vibration signals in prognostics health management (PHM). Aiming to address the severe lack of storage and computational resources and the high delay of transmitting massive vibration data in wireless sensor networks (WSNs), a compression reconstruction method is proposed based on an improved k-singular value decomposition algorithm (K-SVD). First, a substantial amount of vibration signals are processed through the K-SVD dictionary training to obtain a highly comprehensive dictionary. Second, the sparse adaptive matching pursuit algorithm (SAMP) is introduced in the sparse coding part to estimate the sparsity of the vibration signal quickly, which provides a significant advantage in terms of reconstruction accuracy. Finally, the alternating direction method of multipliers (ADMMs) is introduced to speed up the optimization process of dictionary training. The experimental outcomes show that the suggested approach displays enhanced sparsity and reconstruction efficacy in comparison to other techniques under identical compression ratio (CR) circumstances.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3413138