The Characteristic Parameters Extraction of the Drawing Forming Cracks Based on Time-Series Analysis and ICA

2012 ◽  
Vol 239-240 ◽  
pp. 1259-1263
Author(s):  
Zhi Gao Luo ◽  
Jing Jing Zhang ◽  
Jun Li Zhao ◽  
Xu Dong Li
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Time Series Analysis ◽  
Deep Drawing ◽  
Characteristic Parameters ◽  
Distribution Maps ◽  
Part Quality ◽  
Series Analysis ◽  
Parameters Extraction ◽  
Ae Signals

The purpose of the study is to extract the characteristic parameters of the forming crack acoustic emission (AE) signals generated by the metal deep drawing. Time-series analysis and MATLAB were used to adopt independent component analysis (ICA) to isolate the crack AE signals and extracted the characteristic parameters of AE signals. This study isolate the crack AE signals of the drawing parts by the FastICA method based on the maximum negative entropy, the data was processed by MATLAB and the regression model of the various decomposition established by time-series analysis to extract the characteristic parameters of the crack AE signals. The results suggested that this method can isolate the crack AE signals of the deep drawing successfully and can extract the characteristic parameters and distribution maps of the crack AE signals of the metal drawing parts effectively, provide a favorable basis for the judgment of the molding part quality.

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

1989 ◽  
Vol 111 (3) ◽  
pp. 199-205 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Cutting Tool ◽  
Shear Zones ◽  
Parameter Vector ◽  
Model Parameter ◽  
Cutting Tool Wear ◽  
Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

A Time Series Analysis and Neural Network Based Scheme for Fault Diagnosis of Transformers

2015 ◽  
Vol 742 ◽  
pp. 412-418
Author(s):  
Jian Jun Zhang ◽  
Ye Xin Song ◽  
Yong Qu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Characteristic Parameters ◽  
Series Analysis ◽  
Diagnosis Accuracy ◽  
Artificial Neural Network Ann

This research presents a time series analysis and artificial neural network (ANN)-based scheme for fault diagnosis of power transformers, which extracts the characteristic parameters of the faults of the transformer from the results of time series analysis and bases on this basis establishes the corresponding back propagation (BP) neural network to detect the transformer operating faults. The simulation experimental results show that as compared to the related works, the proposed approach effectively integrates the superiority of time series analysis and BP neural network and thus can greatly improve the diagnosis accuracy and reliability.

scholarly journals Time series analysis of tool wear in sheet metal stamping using acoustic emission

2017 ◽  
Vol 896 ◽  
pp. 012030 ◽  
Author(s):  
V. Vignesh Shanbhag ◽  
P. Michael Pereira ◽  
F. Bernard Rolfe ◽  
N Arunachalam
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Sheet Metal ◽  
Sheet Metal Stamping ◽  
Series Analysis ◽  
Metal Stamping

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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