scholarly journals Classification of Three Fatigue Levels for Bills Using Acoustic Frequency Band Energy Patterns

2000 ◽  
Vol 120 (11) ◽  
pp. 1602-1608 ◽  
Author(s):  
Teranishi Masaru ◽  
Omatu Sigeru ◽  
Kosaka Toshihisa
Keyword(s):  
Frequency Band ◽  
Acoustic Frequency ◽  
Band Energy ◽  
Frequency Band Energy

scholarly journals The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanyan Zhang ◽  
Gang Wang ◽  
Chaolin Teng ◽  
Zhongjiang Sun ◽  
Jue Wang
Keyword(s):  
Frequency Band ◽  
Classification Accuracy ◽  
Relative Frequency ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Band Energy ◽  
Frequency Band Energy ◽  
Semg Signals

For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.

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.

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.

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.

Application of Wavelet Packet Energy Spectrum in Micro Motor Fault Diagnosis

2013 ◽  
Vol 726-731 ◽  
pp. 3159-3162
Author(s):  
Sheng Yi Chen ◽  
Gui Tang Wang ◽  
Shou Lei Sun ◽  
Qiang Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Reconstruction Algorithm ◽  
Mechanical Equipment ◽  
Vibration Signals ◽  
Band Energy ◽  
Status Information ◽  
Frequency Band Energy

To diagnosis vibration signals of micro motor in several different fault types a method based on wavelet packet energy spectrum is presented, the energy on each Sub-frequency band, which are Calculated by Wavelet packet decomposition and reconstruction algorithm, are used to normalization process.Under both circumstances of normal working and unmoral working of mechanical equipment,there exist evident differences among the Sub-frequency band energy after the decomposition of wavelet packet, which energy contains a wealth of micro motor running status information and the eigenvectors is structured by the Sub-frequency band energy spectrum can establish energy and Fault mapping relationship.The preliminary experimental results show that it is effective to use the wavelet packet-energy spectrum in micro motor fault diagnosis .

scholarly journals Lubrication State Recognition Based on Energy Characteristics of Friction Vibration with EEMD and SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hai-jie Yu ◽  
Hai-jun Wei ◽  
Jing-ming Li ◽  
Da‐ping Zhou ◽  
Li‐dui Wei ◽  
...  
Keyword(s):  
Frequency Band ◽  
Ensemble Empirical Mode Decomposition ◽  
Experimental Results ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Energy Characteristics ◽  
State Recognition ◽  
Band Energy ◽  
Frequency Band Energy

In order to identify different lubrication states, lubrication experiments were carried out on a Bruker UMT-3 tester. The experimental results show that the frequency band energy characteristics of friction vibration signals are different under different lubrication states. Based on this, a lubrication state recognition method with ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) was proposed. The vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMFs) with the EEMD method. The first six IMF components containing the main friction information were retained to calculate the energy ratio and construct the feature vector. The experimental results show that the mixed lubrication state can be identified by hundred percent, and there is a slight confusion between boundary lubrication and dry friction. The results show that frequency band energy of friction vibration signals is an effective feature to identify different lubrication states, and the proposed method can be used to identify different lubrication states.

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