Signal Analysis of Magnetic Control Seam Tracking Based on the Hilbert-Huang Transform

2014 ◽  
Vol 529 ◽  
pp. 559-563 ◽  
Author(s):  
Bo Hong ◽  
Ai Jun Xu ◽  
Jian Liu ◽  
Xiang Wen Li
Keyword(s):  
Signal Analysis ◽  
Tracking System ◽  
Seam Tracking ◽  
Good Effect ◽  
Time Signal ◽  
Magnetic Control ◽  
Intrinsic Mode Functions ◽  
Welding Seam ◽  
Hilbert Huang Transform ◽  
Time Frequency

According to the fact that the nonlinear magnetic control welding signal is not smooth, this paper proposes a signal extraction and an analytical method of the system based on Hilbert-Huang transform magnetic control arc seam tracking sensor. First, the magnetic control to track the signal motivated by cycle is decomposed into several intrinsic mode functions from high frequency to low frequency component by using the empirical mode decomposition. On the basis of the Hilbert marginal spectrum of each component, distribution of time-frequency transform to each component can effectively restrain cross terms and extract the real-time signal dynamic law reflecting magnetic control seam tracking. This method used in a certain experimental platform for magnetic control arc welding seam tracking sensor platform signal analysis, has produced a good effect and extracted the seam tracking signal, which can offer more valuable information and help to further reveal the frequency and spectrum characteristics of various interference sources in the weld automatic tracking system. Furthermore, It also provides a theoretical basis for establishing the welding signals with excitation source as well as a new nonlinear model.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

2019 ◽  
Vol 9 (10) ◽  
pp. 2017 ◽  
Author(s):  
Juncai Xu ◽  
Bangjun Lei
Keyword(s):  
Instantaneous Frequency ◽  
Data Interpretation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
The Media ◽  
Ground Penetrating

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

Design of DSP-Based Automatic Seam Tracking Underwater System

2014 ◽  
Vol 644-650 ◽  
pp. 845-848
Author(s):  
Fu Yang ◽  
Wen Ming Zhang ◽  
Wan Cai Jiao
Keyword(s):  
Tracking System ◽  
Low Cost ◽  
Human Machine Interface ◽  
Seam Tracking ◽  
Welding Seam ◽  
Automatic Tracking ◽  
Underwater Welding ◽  
Effect Of Water ◽  
Machine Interface ◽  
Hardware Structure

It is high difficult to control the underwater welding because of the effect of water and the leak proofness of the weld devices which is a troubling problem. In this paper, a DSP-based automatic seam tracking system for underwater welding is designed. This system has the advantages of simple hardware structure, low-cost, rich function software, friendly human-machine interface, and easily realizing. And the work of this paper can be used for further research in underwater welding seam automatic tracking.

An Improved HHT Method and its Application in Fault Diagnosis of Roller Bearing

2013 ◽  
Vol 273 ◽  
pp. 264-268 ◽  
Author(s):  
Ling Li Jiang ◽  
Bo Bo Li ◽  
Xue Jun Li
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Low Frequency ◽  
Analysis Method ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Effective Time ◽  
Coefficient Method

Hilbert-Huang transform (HHT) is a very effective time-frequency analysis method, but it has some disadvantages. For example, the dense modal signal cannot be decomposed completely, and redundancy intrinsic mode functions (IMFs) are easy emerging at low frequency, which will cause the distortion of the processing results. In view of the above questions, this study applies the wavelet packet transform for denoising before HHT for improving the dense modal problem, and applies the correlation coefficient method to eliminate the redundancy IMF. The fault diagnostic case of roller bearing shows the effectiveness of the proposed method.

scholarly journals Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 736 ◽  
Author(s):  
Jing Xu ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Daohua Lu ◽  
Baigong Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Coal Mining ◽  
Pattern Identification ◽  
Accurate Information ◽  
Intrinsic Mode Functions ◽  
Identification Rate ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Cutting Pattern

Recently, sound-based diagnosis systems have been given much attention in many fields due to the advantages of their simple structure, non-touching measurement style, and low-power dissipation. In order to improve the efficiency of coal production and the safety of the coal mining process, accurate information is always essential. It is indicated that the sound signal produced during the cutting process of the coal mining shearer contains much cutting pattern identification information. In this paper, the original acoustic signal is first collected through an industrial microphone. To analyze the signal deeply, an adaptive Hilbert–Huang transform (HHT) was applied to decompose the sound to several intrinsic mode functions (IMFs) to subsequently acquire 1024 Hilbert marginal spectrum points. The 1024 time-frequency nodes were reorganized as a 32 × 32 feature map. Moreover, the LeNet-5 convolutional neural network (CNN), with three convolution layers and two sub-sampling layers, was used as the cutting pattern recognizer. A simulation example, with 10,000 training samples and 2000 testing samples, was conducted to prove the effectiveness of the proposed method. Finally, 1971 testing sound series were recognized accurately through the trained CNN and the proposed method achieved an identification rate of 98.55%.

Time-Frequency Analysis for Power System Oscillations

2013 ◽  
Vol 336-338 ◽  
pp. 928-931
Author(s):  
Chia Liang Lu ◽  
Pei Hwa Huang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Frequency Analysis ◽  
Low Frequency ◽  
Normal Operation ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Low Frequency Oscillations

Low frequency oscillations due to the lack of damping may occur in power systems under normal operation and will cause system instability. These oscillations are essentially nonlinear power responses which are difficult to extract the inherent characteristics by the time domain method. This paper aims to analyze nonlinear power responses by using the Hilbert-Huang transform (HHT) which is a time-frequency signal processing method which comprises steps of the empirical mode decomposition and the Hilbert transform. Dynamic power system responses, including generator output power and line power are to be processed by the HHT and a set of intrinsic mode functions and the associated Hilbert spectrum are obtained. The generator with most effects on the system will be accordingly found out through the time-frequency analysis and the power system stabilizer will be placed at the generator. Numerical results from a sample power system are demonstrated to show the validity of the time-frequency approach in the study of power system low frequency oscillations.

Non-Stationary Signal Analysis Based on Hilbert-Huang Transform and Median Filter

2014 ◽  
Vol 1049-1050 ◽  
pp. 1694-1697
Author(s):  
Xiao Li Wang ◽  
Dian Hong Wang
Keyword(s):  
White Noise ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Median Filter ◽  
Low Noise ◽  
Analytic Framework ◽  
Noise Resistance ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Non Stationary Signal

The Hilbert-Huang Transform (HHT) is a new time-frequency analysis with adaptability and orthogonality, but it is rather weak in terms of noise resistance, even low noise can disturb the HHT result greatly. The paper launches an investigation on how noises affect the HHT result and proposes the method to solve the problem. The analytic framework for HHT is first introduced, the feature of the test signal is extracted by HHT. Median filter is adopted to reduce the frequency leakage of certain signal component caused by white noise. The method proposed is experimentally simulated and the results demonstrate its effectiveness.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

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