Development of arbitrary waveform torsional vibration signal generator

Zhang, Qingjie; Lü, Gongxuan; Zhang, Chengyu; Xu, Yi

doi:10.1007/s11235-020-00692-8

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…C 1 , C 2 are constants, and both of them are only affected by health degree k . With the use of medical equipment, its health gradually degenerates, and the overall performance of vibration signal is nonstationary signal [ 21 ]. The characteristic frequencies of different parts of mechanical equipment are different, and the amplitude frequency form of vibration signal a ( f ) can be obtained by the time-frequency conversion tool.…”

Section: Research On the Health Evaluation And Fault Diagnosis Methods Of Medical Imaging Equipment Based On Neural Network Algorithmmentioning

confidence: 99%

“…e mean value and standard deviation of vibration signal in t i ∼ t j period are M and S, respectively. C 1 , C 2 are constants, and both of them are only affected by health degree k. With the use of medical equipment, its health gradually degenerates, and the overall performance of vibration signal is nonstationary signal [21].…”

Section: Health Evaluation Technology Of Medical Imagingmentioning

confidence: 99%

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Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Zhao

Jiang

Gao

2021

Computational Intelligence and Neuroscience

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In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.

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Section: Research On the Health Evaluation And Fault Diagnosis Methods Of Medical Imaging Equipment Based On Neural Network Algorithmmentioning

confidence: 99%

Section: Health Evaluation Technology Of Medical Imagingmentioning

confidence: 99%

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Zhao

Jiang

Gao

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Xueyi et al [13], proposed a train positioning method based on map matching, this method must first have a map database, the database contains the angular velocity corresponding to each coordinate position point, accurately measure angular velocity through fiber optic gyroscope, then compare it with the angular velocity in the map database, furthermore, the current position of the train is determined according to the coordinate position points in the map database. Zhang et al [14], Using phase-sensitive optical time domain reflectometer technology to collect and analyze rail vibration signals, in order to realize the positioning and speed measurement of the train. This technology uses only existing trackside optical cables, no need for additional construction workload, good real-time performance, it has many advantages such as simple installation, long service life, low maintenance cost, strong anti-interference ability, and large-scale distributed detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Railway foreign body vibration signal detection based on wavelet analysis

Wei¹,

Mu²,

Bhatt³

2022

J. vibroeng.

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Based on the wavelet packet analysis method with time-frequency analysis characteristics, the measurement signal of the vibration system is processed for noise reduction, the soft-hard threshold compromise wavelet denoising method used has the advantages of soft threshold and hard threshold denoising, and through the introduction of compromise factors, signal processing can be performed more flexibly in signal analysis. For the denoised signal, the fundamental wavelet time-energy spectrum analysis, the main components of the signal can be clearly displayed, and according to the distribution of its energy in each frequency band, the signal characteristics can be displayed intuitively. Experimental results show: It can be determined that there is a foreign body intrusion incident at a position 520 m away from the monitoring point, rather than a normal train travel incident. In fact, we are walking back and forth at a distance of about 520 m from the monitoring point, simulating the intrusion of illegal foreign objects such as pedestrians and livestock beside the railroad tracks prove that analysis and judgment can be known, the wavelet analysis proposed by the author can realize the monitoring and judgment of some illegal foreign body intrusion incidents such as pedestrians and livestock.

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