scholarly journals A Novel Multidimensional Frequency Band Energy Ratio Analysis Method for the Pressure Fluctuation of Francis Turbine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Weiyu Wang ◽  
Qijuan Chen ◽  
Xing Liang ◽  
Xuhui Yue ◽  
Jinzhou Dou
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Pressure Fluctuation ◽  
Frequency Characteristics ◽  
Energy Ratio ◽  
Francis Turbine ◽  
Band Energy ◽  
Time Frequency ◽  
Frequency Band Energy ◽  
The Impact

The pressure fluctuation has multiple influence on the steady operation of Francis turbine, and the impact degree varies with the operation condition. In this paper, for the analysis of pressure fluctuation in the Francis turbine, a novel feature extraction method of multidimensional frequency bands energy ratio is proposed based on Hilbert Huang Transform (HHT). Firstly, the pressure fluctuation signal is decomposed into intrinsic mode functions (IMFs) by EEMD. Secondly, the Hilbert marginal spectrum is utilized to analyze the frequency characteristics of IMFs. Then, according to the inner frequency of IMFs, each of them is divided into high, medium, or low frequency band which are constructed based on the frequency characteristic of the pressure fluctuations in the Francis turbine. Afterward, the energy ratio of each frequency band to the original signal is calculated, which is to realize the feature extraction of multidimensional frequency band energy ratio. Actual applications verify that this method not only can extract the time-frequency characteristics but also can analyze the condition feature of the pressure fluctuation. It is a novel method for extracting the feature of pressure fluctuation in the Francis turbine.

Prediction Method of Grinding Chatter Based on ARIMA

2014 ◽  
Vol 971-973 ◽  
pp. 1288-1291 ◽  
Author(s):  
Zi Liang Yao ◽  
Min Wang ◽  
Tao Zan ◽  
Guo Fu Liu
Keyword(s):  
Natural Frequency ◽  
Frequency Band ◽  
Prediction Method ◽  
Vibration Monitoring ◽  
Energy Ratio ◽  
Grinding Process ◽  
Band Energy ◽  
Grinding Chatter ◽  
Chatter Prediction ◽  
Frequency Band Energy

The process of grinding chatter is divided into three states: stable grinding state, chatter gestation state and chatter state. The vibration signals of grinding process contain chatter features can correspond well to the changes of grinding process. By analyzing the natural frequency band energy ratio of grinding process, a method of grinding chatter prediction is proposed. Experimental results show that the natural frequency band energy ratio is beyond a certain threshold, chatter occurred, otherwise no chatter happened. The method of grinding prediction can provide reference for vibration monitoring in practice.

Transient Feature Extraction method based on adaptive TQWT sparse optimization

2020 ◽  
Author(s):  
xue liu ◽  
ao sun ◽  
jian hu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Vibration Signal ◽  
Measured Signal ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Transient Feature ◽  
The Impact

Abstract Aiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive Tunable Q-factor Wavelet Transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time-frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

scholarly journals Transient feature extraction method based on adaptive TQWT sparse optimization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xue Liu ◽  
Ao Sun ◽  
Jian Hu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Vibration Signal ◽  
Measured Signal ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Transient Feature ◽  
The Impact

AbstractAiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time–frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

Feature Extraction and Analysis of Passive Underwater Acoustic Signals for Different Species and Quantities of Freshwater Fish

2018 ◽  
Vol 61 (5) ◽  
pp. 1505-1513
Author(s):  
Qunzi Tu ◽  
Hanying Huang ◽  
Lu Li ◽  
Shanbai Xiong
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Peak Frequency ◽  
Main Peak ◽  
Wavelet Packet Decomposition ◽  
Band Energy ◽  
Frequency Band Energy ◽  
Peak Value

Abstract. The underwater signals from one and six breams, crucians, grass carps, and cyprinoids using a hydrophone were preprocessed by Wiener filtering. Three features were extracted: frequency band energy based on wavelet packet decomposition, average mel cepstral coefficient, and main peak frequency and main peak value based on the power spectrum. The effects of fish species and quantity on these features were analyzed. The results show that fish species had significant effects on the frequency band energy based on wavelet packet decomposition, average mel cepstral coefficient, and main peak frequency and main peak value based on the power spectrum. The fish quantity had significant effects on the frequency band energy based on wavelet packet decomposition and main peak value based on the power spectrum, but had no significant effects on the average mel cepstral coefficient and main peak frequency based on the power spectrum. Keywords: Feature extraction, Freshwater fish, Passive underwater acoustic technology, Significance analysis.

scholarly journals Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Junfeng Guo ◽  
Xingyu Liu ◽  
Shuangxue Li ◽  
Zhiming Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Time Frequency ◽  
The Impact

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet

2012 ◽  
Vol 472-475 ◽  
pp. 795-798
Author(s):  
Min Yong Tong
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Automobile Engine ◽  
Band Energy ◽  
Diagnosis Method ◽  
S Model ◽  
Frequency Band Energy

A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

scholarly journals Researching Coal and Rock Character Recognition Based on Wavelet Packet Frequency Band Energy

2014 ◽  
Vol 6 (1) ◽  
pp. 1793-1797 ◽  
Author(s):  
Guanghui Xue ◽  
Xinying Zhao ◽  
Ermeng Liu ◽  
Weijian Ding ◽  
Baohua Hu
Keyword(s):  
Frequency Band ◽  
Character Recognition ◽  
Wavelet Packet ◽  
Band Energy ◽  
Frequency Band Energy

scholarly journals Mechanistic Characteristics of Double Dominant Frequencies of Acoustic Emission Signals in the Entire Fracture Process of Fine Sandstone

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3959 ◽  
Author(s):  
Chuangye Wang ◽  
Xinke Chang ◽  
Yilin Liu ◽  
Shijiang Chen
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Fracture Process ◽  
Low Frequency ◽  
High Amplitude ◽  
Dominant Frequency ◽  
Band Energy ◽  
Ae Signal ◽  
Low Amplitude ◽  
Frequency Band Energy

To determine the intrinsic relationship between the acoustic emission (AE) phenomenon and the fracture pattern pertaining to the entire fracture process of rock, the present paper proposed a multi-dimensional spectral analysis of the AE signal released during the entire process. Some uniaxial compression AE tests were carried out on the fine sandstone specimens, and the axial compression stress–strain curves and AE signal released during the entire fracture process were obtained. In order to deal with tens of thousands of AE data efficiently, a subroutine was programmed in MATLAB. All AE waveforms of the tests were denoised by wavelet threshold firstly. The fast Fourier transform (FFT) and wavelet packet transform (WPT) were applied to the denoised waveforms to obtain the dominant frequency, amplitude, fractal, and frequency band energy ratio distribution. The results showed that the AE signal in the entire fracture process of fine sandstone had a double dominant frequency band of the low and high-frequency bands, which can be subdivided into low-frequency low-amplitude, high-frequency low-amplitude, high-frequency high-amplitude, and low-frequency high-amplitude signals, according to the magnitude. The low-frequency amplitude relevant fractal dimension and the high-frequency amplitude relevant fractal dimension each had turning points that corresponded to significant decreases in the middle and end stages of loading, respectively. The frequency band energy was mainly concentrated in the range of 0–187.5 kHz, and the energy ratios of some bands had different turning points, which appeared before the complete failure of the rock. It is suggested that the multi-dimensional spectral analysis may understand the failure mechanism of rock better.

scholarly journals Experimental Study on Influence of Borehole Arrangement on Energy Conversion and Acoustic Characteristics of Coal-Like Material Sample

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chuanbo Hao ◽  
Zhiyuan Hou ◽  
Fukun Xiao ◽  
Gang Liu
Keyword(s):  
Acoustic Emission ◽  
Energy Conversion ◽  
Frequency Band ◽  
Acoustic Emission Signal ◽  
Conversion Ratio ◽  
Acoustic Characteristics ◽  
Band Energy ◽  
Emission Signal ◽  
Material Sample ◽  
Frequency Band Energy

This paper examines the effects of borehole arrangement on the failure process of coal-like materials based on its energy conversion and acoustic characteristics from the perspectives of energy, AE energy, AE spectrum, and frequency band. Findings from the study revealed that the presence of borehole can significantly reduce the conversion ratio and growth rate of elastic energy during the loading of coal-like material sample and delay the release of internal energy of the sample. Also, it can reduce the frequency band energy of the main frequency of acoustic emission signal but has little effect on the size and richness of the peak frequency of acoustic emission signal. The practice that makes drilling diameter and depth increase stepwise can minimize the elastic energy conversion ratio, the growth rate, and the main frequency band energy of acoustic emission signal of coal-like material sample and postpone the internal energy release of the sample to the greatest extent, so as to enrich the richness of the secondary frequency of acoustic emission signal. The results of this study have certain guiding significance for the layout of pressure relief boreholes in the production process of coal mines.

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