Multi-scale shape prior using wavelet packet representation and independent component analysis

2007 ◽  
Author(s):  
Rami Zewail ◽  
Ahmed Elsafi ◽  
Nelson Durdle
Keyword(s):  
Independent Component Analysis ◽  
Wavelet Packet ◽  
Component Analysis ◽  
Independent Component ◽  
Shape Prior ◽  
Multi Scale

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

scholarly journals Knowledge-guided multi-scale independent component analysis for biomarker identification

2008 ◽  
Vol 9 (1) ◽  
pp. 416 ◽  
Author(s):  
Li Chen ◽  
Jianhua Xuan ◽  
Chen Wang ◽  
Ie-Ming Shih ◽  
Yue Wang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Biomarker Identification ◽  
Multi Scale

scholarly journals MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

2020 ◽  
Vol 1 (1) ◽  
pp. 25-35
Author(s):  
Abolfazl Hajisami ◽  
Dario Pompili
Keyword(s):  
Independent Component Analysis ◽  
Multiple Scales ◽  
Digital Signal ◽  
Component Analysis ◽  
Scale Space ◽  
Independent Component ◽  
Signal Denoising ◽  
Multi Scale ◽  
Information Preservation ◽  
Bank Structure

Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

scholarly journals Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Leilei Jia ◽  
Xianli Wang
Keyword(s):  
Independent Component Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Component Analysis ◽  
Rotating Machinery ◽  
Independent Component ◽  
Blind Separation ◽  
Denoising Method ◽  
Average Value ◽  
Fast Independent Component Analysis

The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

scholarly journals Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401881103 ◽  
Author(s):  
Lizheng Pan ◽  
Dashuai Zhu ◽  
Shigang She ◽  
Aiguo Song ◽  
Xianchuan Shi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Kernel Function ◽  
Wavelet Packet ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Aiming at the problem of gear fault diagnosis, in order to effectively extract the features and improve the accuracy of gear fault diagnosis, the method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion is proposed in this research. The proposed wavelet-packet independent component analysis feature extraction method can effectively combine the advantages of wavelet packet and independent component analysis methods and acquire more comprehensive feature information. Besides, the proposed kernel-function-fusion support vector machine can well integrate the advantage characteristics of each kernel function. The energy features of wavelet packet coefficients are acquired with four-layer wavelet packet decomposition and then the extracted energy features are further optimized by the independent component analysis method. The kernel-function-fusion support vector machine method is adopted to realize the gear fault diagnosis. Two kernel function models with the best self-classification accuracy are employed to serve the gear fault diagnosis corporately. The test samples are primarily classified by the main kernel function model, and then some samples are selected to be reclassified with the other kernel function model. Finally, the two kernel function models cooperate to determine the type of test samples. The comparison investigations demonstrate that the proposed method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion achieves very high diagnosis accuracy.

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