Instantaneous feature extraction of non-stationary signal based on complex Morlet wavelet

2011 ◽  
Author(s):  
Ruan Huailin ◽  
Xu Jiren
Keyword(s):  
Feature Extraction ◽  
Morlet Wavelet ◽  
Complex Morlet Wavelet ◽  
Non Stationary Signal

Envelope Analysis Based on the Combination of Morlet Wavelet and Kurtogram

2012 ◽  
Vol 490-495 ◽  
pp. 305-308
Author(s):  
Yu Liang ◽  
Yu Guo ◽  
Chuan Hui Wu ◽  
Yan Gao
Keyword(s):  
Frequency Band ◽  
Filter Banks ◽  
Morlet Wavelet ◽  
Center Frequency ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Band Pass ◽  
Complex Morlet Wavelet

Envelope analysis based on the combination of complex Morlet wavelet and Kurtogram have advantages of automatic calculation of the center frequency and bandwidth of required band-pass filter. However, there are some drawbacks in the traditional algorithm, which include that the filter bandwidth is not -3dB bandwidth and the analysis frequency band covered by the filter-banks are inconsistent at different levels. A new algorithm is introduced in this paper. Through it, both optimal center frequency and bandwidth of band-pass filter in the envelop analysis can be obtained adaptively. Meanwhile, it ensures that the filters in the filter-banks are overlapped at the point of -3dB bandwidth and the consistency of frequency band that the filter-banks covered.

Thermal Overload Protection for AC Asynchronous Motor Based on Complex Morlet Wavelet

2015 ◽  
Vol 740 ◽  
pp. 364-367
Author(s):  
Su Wang ◽  
Lei Sun ◽  
Wei Cong Huang
Keyword(s):  
Asynchronous Motor ◽  
Morlet Wavelet ◽  
Algorithm Analysis ◽  
Full Wave ◽  
Overload Protection ◽  
Dc Component ◽  
Complex Morlet Wavelet ◽  
Fourier Algorithm ◽  
Wavelet Algorithm ◽  
Better Than

Conventionally, the fault signal of motor thermal overload in a non-periodic component is not effectively filtered with Full-wave Fourier Algorithm (or FFA). In this paper, a design which combined Complex Morlet Wavelet Algorithm with Subtraction (or CMWAS) filter is presented. The design gives system model of overload and algorithm analysis It is verified that the new algorithm is better than the FFA algorithm in terms of filtering decaying DC component.

scholarly journals Screening of sleep movement disorders via EMG signal analysis

2021 ◽  
Author(s):  
Mehrnaz Shokrollahi
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Movement Disorders ◽  
Dictionary Learning ◽  
Signal Analysis ◽  
Compact Set ◽  
Emg Signal ◽  
Increased Risk ◽  
Non Stationary Signal ◽  
Representation Techniques

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

Feature Extraction Accuracy Improvement of Acoustic Signals Based on Reassigned Wavelet Scalogram

2011 ◽  
Vol 55-57 ◽  
pp. 2065-2068
Author(s):  
Pan Li ◽  
Ping He ◽  
Hui Qi Sun ◽  
Wei Shang ◽  
Nan Xiang Sun
Keyword(s):  
Feature Extraction ◽  
High Reliability ◽  
Acoustic Signals ◽  
Morlet Wavelet ◽  
Frequency Error ◽  
Accuracy Improvement ◽  
Characteristic Frequencies ◽  
Location Method ◽  
Simulation Results ◽  
Morlet Wavelet Transform

Based on the wavelet scalogram obtained by Morlet wavelet transform and hard threshold de-noising filtering for typical acoustic emission signals, region segmented location method is introduced to get the number and accurate values of the characteristic frequencies, therefore the error induced by misjudgment and misreading can be avoided effectively. Then considering the weakness of large characteristic frequency error in Morlet wavelet scalogram, the feature extraction accuracy has been improved by combing region segmented location method and reassigned wavelet scalogram. Simulation results show that the proposed method has the merits of well rapidity, high reliability and briefness, hence can realize high precision feature extraction and has great practical value.

Bearing Fault Detection Based on Order Tracking and Complex Morlet Wavelet Transform

2011 ◽  
Vol 474-476 ◽  
pp. 639-644 ◽  
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Morlet Wavelet ◽  
Experimental Result ◽  
Bearing Fault ◽  
Multi Scale ◽  
Order Tracking ◽  
Continuous Complex ◽  
Complex Morlet Wavelet ◽  
Morlet Wavelet Transform ◽  
Envelope Spectrum

A new approach to bearing fault diagnosis under run-up based on order tracking and continuous complex Morlet wavelet transform demodulation technique is presented. The non-stationary vibration signal is first transformed from the time domain transient signal to angle domain stationary one using order tracking technique. Then the continuous complex Morlet wavelet transform is applied to the angle domain re-sampled signal and the complex Morlet wavelet transform based multi-scale envelope spectrum is obtained. The experimental result shows that order tracking and complex Morlet wavelet transform based multi-scale envelope spectrum can effectively diagnosis bearing localized fault.

Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

2011 ◽  
Vol 36 (8) ◽  
pp. 2146-2153 ◽  
Author(s):  
Yonghua Jiang ◽  
Baoping Tang ◽  
Yi Qin ◽  
Wenyi Liu
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Extraction Method ◽  
Morlet Wavelet ◽  
Feature Extraction Method

scholarly journals APPLICATION OF ISO-FREQUENCY MAPS TO TIME-LAPSE DATA

2017 ◽  
Vol 35 (3) ◽  
Author(s):  
Wagner Moreira Lupinacci ◽  
Anderson Peixoto de Franco ◽  
Fernando Vizeu Santos ◽  
Marco Antonio Cetale Santos
Keyword(s):  
Wavelet Transform ◽  
Energy Density ◽  
Curvelet Transform ◽  
Time Lapse ◽  
Morlet Wavelet ◽  
Seismic Exploration ◽  
Time Frequency ◽  
Advantages And Disadvantages ◽  
Morlet Wavelet Transform ◽  
Non Stationary Signal

ABSTRACT. Time-frequency transforms are widely used in seismic exploration. These transforms enable analysis of the energy density of a non-stationary signal as functions of amplitude, time and frequency. The representation of energy density is not unique, and each transform has its advantages and disadvantages. The choice of which transform should be used depends on the application. In this paper, we propose a new way to analyze time-lapse anomalies using iso-frequency panels obtained by time-frequency transforms. We compared the iso-frequency panels of the Morlet Wavelet Transform and Choi-Williams Distribution. These panels revealed different characteristics and can provide additional information for the interpretation of time-lapse anomalies. We used seismic data from the Marimbá field of the Campos Basin, Brazil, for which base and monitor acquisitions were held in 1984 and 1999, respectively. We also used a special filtering approach to enhance seismic resolution and remove noise, whereby we applied the curvelet transform to remove noise, and employed a tool to correct the residual moveout and inverse Q filtering for attenuation correction. Then we analysed the time-lapse anomalies using iso-frequency panels. The main time-lapse anomalies appeared in the form of clouds in the iso-frequency panels obtained by the Morlet Wavelet Transform approach. Iso-frequency panels obtained by Choi-Williams Distribution showed a higher sensitivity and resolution for analyzing the anomalies. Our results show the great potential of these transforms for visualization of time-lapse anomalies. Keywords: time-lapse anomalies, spectrogram, Morlet Wavelet Transform, Choi-Williams Distribution. RESUMO. Transformadas tempo-frequência são amplamente utilizadas na exploração sísmica. Estas transformadas permitem a análise da densidade de energia de um sinal não-estacionário como funções de amplitude, tempo e frequência. A representação da densidade de energia de um sinal não é única, e cada transformada tem suas vantagens e desvantagens. A escolha da transformada que deve ser usada depende da aplicação. Neste artigo, propomos uma nova abordagem para analisar anomalias de dados time-lapse usando painéis iso-frequência obtidos através de transformadas tempo-frequência. Comparamos os painéis iso-frequência obtidos com a Transformada Wavelet de Morlet e a Distribuição de Choi-Williams. Estes painéis revelaram diferentes características que podem fornecer informações adicionais para a interpretação de anomalias time-lapse. Os dados sísmicos utilizados foram do Campo de Marimbá da Bacia de Campos, Brasil, os quais as aquisições base e monitor foram realizadas em 1984 e 1999, respectivamente. Antes da análise dos painéis iso-frequência, usamos um workflow para melhorar a resolução sísmica e a razão sinal-ruído. Neste workflow, aplicamos a Transformada Curvelet para remover ruídos aleatórios e coerentes, uma ferramenta para corrigir o moveout residual e um Q-filter para correção dos efeitos da atenuação. Após este workflow, as principais anomalias time-lapse apareceram na forma de nuvens nos painéis iso-frequência da Transformada Wavelet de Morlet. Já os painéis iso-frequência da Distribuição de Choi-Williams apresentaram uma maior sensibilidade e resolução para análise dessas anomalias. Os resultados mostraram o grande potencial dessas transformadas para a visualização e interpretação de anomalias time-lapse. Palavras-chave: anomalias time-lapse, espectrograma, Transformada Wavelet de Morlet, Distribuição Choi-Williams. 

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