scholarly journals An Effective Gear Fault Diagnosis Method Based on Singular Value Decomposition and Frequency Slice Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fu-Cheng Zhou ◽  
Gui-Ji Tang ◽  
Yu-Ling He
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Hankel Matrix ◽  
Engineering Application ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Gear Fault ◽  
Value Decomposition

The ability of the frequency slice wavelet transform (FSWT) to distinguish the fault feature is weak under the condition of strong background noise; in order to solve this problem, a fault feature extraction method combining the singular value decomposition (SVD) and FSWT was proposed. Firstly, the Hankel matrix was constructed using SVD, based on which the SVD order was determined according to the principle of the single side maximum value. Then, the denoised signal was further processed by the FSWT to obtain the time-frequency spectrum of the passband. Finally, the detailed analysis was carried out in the time-frequency area with concentrated energy, and the signal was reconstructed by the inverse-FSWT. The processing effect for the pitting corrosion and the tooth broken faults of the gears shows that the faulty feature can be extracted effectively from the envelope spectrum of the reconstructed signal, which means the proposed method is able to help obtain a qualified result and has the potential to be carried out for the practical engineering application.

A Feature Extraction Method of Gear Fault Based on the SVD EMD and Morphology

2013 ◽  
Vol 433-435 ◽  
pp. 477-482 ◽  
Author(s):  
Gao Yan Hou ◽  
Yong Lv ◽  
Hao Huang ◽  
Yi Zhu
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Extraction Method ◽  
Singular Value ◽  
Background Signal ◽  
Feature Extraction Method ◽  
Filtering Method ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Value Decomposition

In order to extract the weak signal from strong background signal characteristics, a feature extraction method combined of the singular value decomposition (SVD), empirical mode decomposition (EMD) and mathematical morphology was proposed. The signal got through the singular value decomposition first. Next took the average value of the decomposed main components. And carried on the empirical mode decomposition and selected the main component to summate and refactor. Then morphological difference filter was used to extract the frequency characteristics of the fault signal. The results of numerical simulation test and gear fault simulation experiments show that the proposed method can clearly extract the frequency characteristics of weak signal from strong background signal and noise. Comparison has been done with the results of singular value decomposition (SVD) and morphological filtering method and empirical mode decomposition form of filtering method. It proves the effectiveness of the proposed method.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

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