A new method for arrival time determination of impact signal based on HHT and AIC

Liu, Mingzhou; Yang, Jiangxin; Cao, Yanan; Fu, Weinan; Cao, Yanlong

doi:10.1016/j.ymssp.2016.10.003

Cited by 27 publications

(11 citation statements)

References 16 publications

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“…where K is the range through all sampling signal points in the time window. According to references [21,28,31,34], the echo signal received by sensors can be considered as the pure echo signal with added Gaussian white noise, and the noise is uncorrelated with the echo signal. Thus, the…”

Section: Accurate Arrival-time Determination Of Echo Signal Based On Bicmentioning

confidence: 99%

“…The information criteria function method, which was utilized to determine the location of acoustic emissions (AEs) from concrete by Kurz et al [20], finds the envelope of the signal first, and then determines the arrival time of the echo by calculating the Akaike information criterion (AIC) function of the envelope of the signal. Furthermore, Liu et al utilize the Hilbert-Huang Transform (HHT) and AIC function to determine the arrival time of the impact signal [21]. They divide the signal into two local stationary auto-regressive (AR) processes that correspond to the noise part before the signal arrives and the signal itself.…”

mentioning

confidence: 99%

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Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Zhang

Gao

et al. 2020

Sensors

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The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

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Section: Accurate Arrival-time Determination Of Echo Signal Based On Bicmentioning

confidence: 99%

mentioning

confidence: 99%

Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Zhang

Gao

et al. 2020

Sensors

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“…At present, the extraction methods of fault features have been developed from conventional methods (such as fast Fourier transform, power spectrum estimation, time-frequency analysis, and axis trajectory) to a higher level (angle domain analysis, holographic spectrum and fractal dimension, etc.) [26]. For example, the holographic spectrum theory, which combines the amplitude, frequency, phase, angular momentum, and other information of mechanical vibration for research and judgment, is of great significance for improving the diagnosis level of mechanical faults.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Feature Extraction and Recognition Method Based on EEMD Superresolution Sparse Decomposition

Zhang

Raja

Deng³

et al. 2022

Shock and Vibration

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This paper aims at the shortcomings of the current conventional processing methods of bearing fault vibration signals in improving signal-to-noise ratio, fine feature extraction, and recognition. A feature extraction and recognition method of abnormal vibration signals based on Ensemble Empirical Mode Decomposition (EEMD) superresolution sparse decomposition is designed. First of all, the superresolution sparse decomposition method is used to refine the set of IMF components of vibration signals after EEMD decomposition. Secondly, the features of the set are extracted and their corresponding energy entropy is calculated. Thirdly, the classification and recognition are carried out. Finally, the effectiveness and feasibility of the method are verified by experiments. It has been proved that this method can better realize the denoising and fine processing aimed at abnormal vibration signals. It has certain theoretical significance and applied value.

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“…The information criteria function method, which is used to determine the location of acoustic emissions (AEs) from concrete by Kurz et al, is to find the envelope of the signal first, and then determine the arrival time of the echo by calculating the Akaike information criterion (AIC) function of the envelope of the signal [10]. Furthermore, Liu et al utilize HHT and the AIC function to determine the arrival time of impact signal [11]. They divide the signal into two local stationary autoregressive processes, which correspond to the noise part that is before signal arriving, and the signal itself.…”

Section: Introductionmentioning

confidence: 99%

Flow Measurement of Natural Gas in Pipeline Based on 1D-Convolutional Neural Network

Zhang¹

2020

IJCIS

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Time-difference method is a vitally significant algorithm for measuring natural gas flow with ultrasonic gas flowmeter. The key of this algorithm is to accurately measure the arrival time of ultrasonic signal. However, it is difficult to determine the feature points corresponding to the arrival time stably and accurately. To solve this problem, based on great feature recognition ability of deep learning, one-dimensional-convolutional neural network (1D-CNN) is utilized to determine the arrival time of ultrasonic signal according to the feature of the arrival time. First of all, a dataset, which includes different features such as different arrival time, different signal-to-noises (SNRs), etc., is used as a training set to train the 1D-CNN. Then, based on the size of the training set, an 1D-CNN is designed which includes three convolution and pooling layers and one fully connected layer to determine the arrival time, and the gas flow rate is calculated. To verify this method, an experimental ultrasonic gas flowmeter system is developed. By comparing with the typical method of determining arrival time, most of the deviations distribute close to zero and less than ±5 us using the proposed 1D-CNN, which verifies the effectiveness of the proposed 1D-CNN method.

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A new method for arrival time determination of impact signal based on HHT and AIC

Cited by 27 publications

References 16 publications

Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Bearing Fault Vibration Signal Feature Extraction and Recognition Method Based on EEMD Superresolution Sparse Decomposition

Flow Measurement of Natural Gas in Pipeline Based on 1D-Convolutional Neural Network

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