Open Access

ARTICLE

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

Guangfei Jia^*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang

School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China

* Corresponding Author: Guangfei Jia. Email:

Structural Durability & Health Monitoring 2023, 17(5), 383-405. https://doi.org/10.32604/sdhm.2023.026885

Received 30 September 2022; Accepted 24 May 2023; Issue published 07 September 2023

Abstract

With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the speed is obtained by fitting. Then, the speed is used for equal angle sampling of time-domain signals, and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity, and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals. The accuracy of the CPP method is verified by simulated, experimental signals and compared with some existing IF extraction methods. The COT method also shows good signal stabilization ability through simulation and experiment. Finally, combined with the comparative test of the other two algorithms and four noise reduction effect indicators, the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability. It is shown that this method is an effective multi-signal noise reduction method.

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