Independent component analysis as applied to vibration source separation and fault diagnosis

A novel fast algorithm for lndependent Component Analysis is introduced, which can be used for blind source separation and machine fault diagnosis feature extraction. It is shown how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The purpose of this paper is to review the application of blind source separation in the machine fault diagnosis,including the following aspects: noise elimination and extraction of the weak signals,the separation of multi-fault sources,redundancy reduction,feature extraction and pattern classification based on independent component analysis. And its application in machine fault diagnosis is illustrated by the examples. In addition, some prospects about using blind source separation for machine fault diagnosis are discussed.

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Turbine vibration source separation based on Independent Component Analysis

2010 IEEE International Conference on Mechatronics and Automation ◽

10.1109/icma.2010.5588775 ◽

2010 ◽

Cited By ~ 2

Author(s):

Huan Yang ◽

Yibing Liu ◽

Hongwen An ◽

Yanbing Zhou

Keyword(s):

Independent Component Analysis ◽

Source Separation ◽

Component Analysis ◽

Independent Component ◽

Vibration Source

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-357 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 357-1-357-6

Author(s):

Luisa F. Polanía ◽

Raja Bala ◽

Ankur Purwar ◽

Paul Matts ◽

Martin Maltz

Keyword(s):

Independent Component Analysis ◽

Spectral Reflectance ◽

Source Separation ◽

Principal Component ◽

Component Analysis ◽

Previous Method ◽

Independent Component ◽

Light Transport ◽

Camera Model ◽

Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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