An Optimized Vibration Signal Compressed Sensing Based on Phase Blocking K-SVD Algorithm for Marine Diesel Engine Cylinders

The condition of the marine diesel engine cylinder can be monitored by analyzing the vibration collected from the cylinder head. However, the large amount of vibration data brings a series of storage and transmission problems. Compressed sensing is an effective way to address these issues, but the classical K-SVD compressed sensing algorithm has a low compression ratio and poor reconstruction accuracy in this application. Considering the steady working phase of the marine diesel engine, a compressed sensing algorithm based on phase blocking K-SVD (PB-KSVD) is proposed. Firstly, the vibration signal is divided into blocks based on the phase information extracted from the top dead center (TDC) signal. Then, the K-SVD algorithm is employed to train the corresponding dictionaries for each cylinder. Finally, the trained dictionaries are used to reconstruct the compressed data according to the phase blocks. Compared with the classical K-SVD compressed sensing method, PB-KSVD achieves high-precision reconstruction in both the time and frequency domains under a low compression ratio.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3527622
Citations
6
Primary Topic
Photoacoustic and Ultrasonic Imaging
Type
article
Field-Weighted Citation Impact
4.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Optimized Vibration Signal Compressed Sensing Based on Phase Blocking K-SVD Algorithm for Marine Diesel Engine Cylinders

Yong Ren, Xiaodong Liu, Xiangwang Hou, Dongmei Chen et al.
6 citations
IEEE Transactions on Instrumentation and Measurement
Photoacoustic and Ultrasonic Imaging
4.04
article

An Optimized Vibration Signal Compressed Sensing Based on Phase Blocking K-SVD Algorithm for Marine Diesel Engine Cylinders

Yong Ren, Xiaodong Liu, Xiangwang Hou, Dongmei Chen, Yue Sun, Lan Ding, Tao Huang, Hao Liu
article en
6 citations

Abstract

The condition of the marine diesel engine cylinder can be monitored by analyzing the vibration collected from the cylinder head. However, the large amount of vibration data brings a series of storage and transmission problems. Compressed sensing is an effective way to address these issues, but the classical K-SVD compressed sensing algorithm has a low compression ratio and poor reconstruction accuracy in this application. Considering the steady working phase of the marine diesel engine, a compressed sensing algorithm based on phase blocking K-SVD (PB-KSVD) is proposed. Firstly, the vibration signal is divided into blocks based on the phase information extracted from the top dead center (TDC) signal. Then, the K-SVD algorithm is employed to train the corresponding dictionaries for each cylinder. Finally, the trained dictionaries are used to reconstruct the compressed data according to the phase blocks. Compared with the classical K-SVD compressed sensing method, PB-KSVD achieves high-precision reconstruction in both the time and frequency domains under a low compression ratio.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Wuhan Ship Development & Design Institute (CN), Marine Design & Research Institute of China (CN), Shandong University of Science and Technology (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Life below water
Openalex Percentile: Top 7%
Photoacoustic and Ultrasonic Imaging
4.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.