Feature Extraction of Acoustic Emission Signals from Low Carbon Steel Pitting Based on Independent Component Analysis and Wavelet Transforming
نویسندگان
چکیده
According to the characteristics of low-carbon steel pitting acoustic emission signal, one new characteristic analysis-WICA is proposed. The main idea of WICA is that based on the independent component analysis blind source separation technology as well as the wavelet transformation decomposition technology, proposed determines the low-carbon steel pitting source number according to the energy characteristic vector the method. The experiment results show that to some extent, WICA can overcome the difficulties caused by the uncertainty of low-carbon steel pitting independent source and get better results of low-carbon steel pitting acoustic emission signal pattern recognition.
منابع مشابه
A review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملIn-Situ Observation and Acoustic Emission Analysis for Corrosion Pitting of MgCl2 Droplet in SUS304 Stainless Steel
Acoustic emission with near-field detection and optical video microscope monitoring was proposed to investigate the pitting corrosion caused by a small magnesium-chloride droplet. Four types of the detected waveforms were characterized by the wavelet analysis to investigate the corrosion of work-hardened SUS304 stainless steel. After high-activity AE signals observed, the corrosion product shee...
متن کاملComparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...
متن کاملCluster Analysis of Acoustic Emission Signals for Carbon/Epoxy Composite in Four-point Bending Test (RESEARCH NOTE)
Due to the extensive use of composites in various industries and the fact that defects reduce ultimate strength and efficiency during operation, detection of failures in composite parts is very important. The aim of this paper is to use Acoustic Emission (AE) non-destructive method in four-point bending test of carbon/epoxy composite to analyze and examine the failure mechanisms. This method is...
متن کامل