Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

Gong, Cheng; Xie, Dingbang; Guo, Cuiping; Kherbachi, Sonia

doi:10.32604/cmc.2021.016436

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Kim et al proposed empirical mode decomposition based on adaptive variable-scale frequencyshift band-pass stochastic resonance denoising, a denoising method of a vibration signal with an interval threshold of empirical mode decomposition [8]. Gong et al proposed a rolling bearing fault feature extraction method based on the improved envelope spectrum based on EMD and spectrum kurtosis, namely, the holographic spectrum technique for mechanical fault diagnosis [9]. Shriram et al proposed the method of the multi-scale envelope order spectrum to evaluate the health status of the mechanical system under variable speed conditions and used the instantaneous frequency obtained by the empirical mode decomposition method to detect the state degradation of bearings [10] signal [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Kang

2022

Mathematical Problems in Engineering

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In order to solve the problem of low positioning accuracy of mechanical fault diagnosis, a polarization GPR imaging reconstruction algorithm based on the MMV model was proposed. The algorithm was mainly based on the joint processing of the measured data of multiple polarization channels to achieve the reconstruction of the reflectance of the detection scene corresponding to each polarization channel. The simulation data processing results based on FDTD showed that compared with the traditional SMV model polarization imaging algorithm, the proposed imaging algorithm could improve the accuracy of target location reconstruction and the ability of background clutter suppression significantly. Compared with the SMV model, TCR obtained by the MMV model increased by 30%. As for the imaging results at different noise ratios, TCR obtained by the MMV model was 10% higher than that obtained by the SMV model. And when the ratio of available real data samples decreased to 25%, the sample data generation based on the adversarial generation network could greatly improve the classification accuracy of the fault diagnosis model. It could realize the detection of the target better, so as to locate faults accurately.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Kang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

Cited by 1 publication

References 23 publications

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

Application of Multi-Measurement Vector Based on the Wireless Sensor Network in Mechanical Fault Diagnosis

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