scholarly journals Toward the detection of gravitational waves under non-Gaussian noises II. Independent component analysis

2016 ◽  
Vol 92 (8) ◽  
pp. 336-345 ◽  
Author(s):  
Soichiro MORISAKI ◽  
Jun’ichi YOKOYAMA ◽  
Kazunari EDA ◽  
Yousuke ITOH
Keyword(s):  
Independent Component Analysis ◽  
Gravitational Waves ◽  
Component Analysis ◽  
Independent Component ◽  
Non Gaussian

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.

Application of Fault Diagnosis Method Based on cICA to Gearbox

2014 ◽  
Vol 664 ◽  
pp. 148-152
Author(s):  
Shuang Xi Jing ◽  
Song Tao Guo ◽  
Jun Fa Leng ◽  
Xing Yu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Early Application ◽  
Independent Components ◽  
Gear Fault ◽  
Constrained Independent Component Analysis ◽  
Non Gaussian

Constrained independent component analysis (cICA) is a new theory and new method derived from the independent component analysis (ICA).It can extract the desired independent components (ICs) from the data based on some prior information, thus overcoming the uncertainty of the traditional ICA. Early gearbox fault signals is often very weak ,characterized by non-Gaussian,low signal-to-noise ratio (SNR), which make the existing diagnosis methods in the diagnosis of early application restricted. In this paper,cICA algorithm is applied to gear fault diagnosis. Through the case studies verify the feasibility of this method to extract the desired independent components (ICs), indicating the applicability and effectiveness of the method.

scholarly journals Orthogonal Approach to Independent Component Analysis Using Quaternionic Factorization

2020 ◽  
Author(s):  
Adam Borowicz
Keyword(s):  
Independent Component Analysis ◽  
Additive Noise ◽  
Component Analysis ◽  
Independent Component ◽  
Actual Distribution ◽  
Rotation Matrices ◽  
Fastica Algorithm ◽  
Non Gaussian ◽  
Ica Algorithm ◽  
Source Signals

Abstract Independent component analysis (ICA) is a popular technique for demixing multi-channel data. The performance of typical ICA algorithm strongly depends on many factors such as the presence of additive noise, the actual distribution of source signals, and the estimated number of non-Gaussian components. Often a linear mixing model is assumed and the source signals are extracted by proceeding data whitening followed by a sequence of plane (Jacobi) rotations. In this article, we develop a four-unit, symmetric algorithm, based on the quaternionic factorization of the rotation matrices and the Newton-Raphson iterative scheme. Unlike conventional rotational techniques such as the JADE algorithm, our method exploits 4 x 4 rotation matrices and uses negentropy approximation as a contrast function. Consequently, the proposed method can be adapted to a given data distribution (e.g. super-Gaussians) by selecting the appropriate non-linear function that approximates the negentropy. Compared to the widely used, symmetric FastICA algorithm, the proposed method does not require an orthogonalization step and offers better numerical stability in the presence of multiple Gaussian sources.

Application of Independent Component Analysis for Sound Source Separation

2007 ◽  
Author(s):  
Chin An Tan ◽  
Arvind Gupta ◽  
Shaungqing Li
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Learning Algorithm ◽  
Linear Representation ◽  
Component Analysis ◽  
Independent Component ◽  
Biomedical Signal Processing ◽  
Strong Basis ◽  
Non Gaussian ◽  
Linear Mixtures

In this paper, experiments on the application of the independent component analysis (ICA) technique to separate unknown source signals are reported. ICA is one of the fastest growing fields in signal processing with applications to speech recognition systems, telecommunications, and biomedical signal processing. It is a data-transformation technique that finds independent sources of activity from linear mixtures of unknown independent sources. The statistical method to measure independence is to find a linear representation of the non-Gaussian data so that the components are as independent as possible and the mutual information between them is minimum. Although extensive simulations have been performed to demonstrate the power of the learning algorithm for the problems of instantaneous mixing and un-mixing of sources, its application to the noise diagnosis and separation in an industrial setting has not been considered. Noise separation in machinery has a strong basis in the “cocktail problem” in which it is difficult to separate/isolate the voice of a person in a room filled with competing voices and noises. The experiments conducted consist of separating several artificially generated sources of noise. Our results demonstrate that ICA can be effectively employed for such kinds of applications. The underdetermined problem in which there are fewer sensors than sources in the ICA formulation is also examined by applying a time-invariant linear transformation of the acquired signals to identify a single source.

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