scholarly journals Signal Analysis and Filtering using one Dimensional Hilbert Transform

2020 ◽  
Vol 1706 ◽  
pp. 012107
Author(s):  
V Dankan Gowda ◽  
G Naveena Pai ◽  
S B Sridhara ◽  
K S Shashidhara ◽  
Gangadhara
Keyword(s):  
Hilbert Transform ◽  
Signal Analysis ◽  
One Dimensional

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Fault Detection of Water Hydraulic Motor by Demodulated Vibration Signal Analysis with the Hilbert Transform and Genetic Algorithm

2011 ◽  
Vol 39 (2) ◽  
pp. 102905 ◽  
Author(s):  
M. R. Mitchell ◽  
R. E. Link ◽  
Qingjun Wang ◽  
Hanxin Chen ◽  
Patrick Chua ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Hydraulic Motor ◽  
Water Hydraulic ◽  
Vibration Signal Analysis ◽  
The Hilbert Transform

Signal Denoising Method Based on KICA by Noise Components

2013 ◽  
Vol 329 ◽  
pp. 269-273
Author(s):  
Ling Li Jiang ◽  
Ping Li ◽  
Bo Zeng
Keyword(s):  
Independent Component Analysis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Independent Component ◽  
Signal Denoising ◽  
Denoising Method ◽  
One Dimensional ◽  
Kernel Independent Component Analysis ◽  
Multidimensional Signal

Denoising is an essential part of fault signal analysis. This paper proposes a kernel independent component analysis (KICA)-based denoising method for removing the noise from vibration signal. By introducing noise components of the observed signal, one-dimensional observed signal is extended to multi-dimensional signal. Then performing KICA on multidimensional signal, the noise in the observed signal consistent with the introduced noise will be removed that achieve the purpose of denosing. The effectiveness of the proposed method is demonstrated by the case study.

One Dimensional Large Signal Analysis for Helix TWTs and Multicavity Klystrons

1994 ◽  
Vol 40 (1) ◽  
pp. 11-15
Author(s):  
E V R Narasimha Rao ◽  
D S Venkateswarlu
Keyword(s):  
Signal Analysis ◽  
Large Signal ◽  
One Dimensional

scholarly journals A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Gaowei Xu ◽  
Tianhe Ren ◽  
Yu Chen ◽  
Wenliang Che
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Eeg Signal ◽  
Learning Methods ◽  
One Dimensional ◽  
Eeg Signal Analysis

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

A one-dimensional discrete Hilbert transform for interferometric phases

2005 ◽  
Author(s):  
Ribun Onodera ◽  
Yukihiro Ishii
Keyword(s):  
Hilbert Transform ◽  
Discrete Hilbert Transform ◽  
One Dimensional
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