355532 ESTIMATION OF GAUGE IRREGULARITY USING INDEPENDENT COMPONENT ANALYSIS(Condition Monitoring,Technical Session)

2009 ◽  
Vol 2009 (0) ◽  
pp. _355532-1_-_355532-6_
Author(s):  
Hiroki Nakamura ◽  
Kimihiko Nakano
Keyword(s):  
Independent Component Analysis ◽  
Condition Monitoring ◽  
Component Analysis ◽  
Independent Component ◽  
Technical Session

An Independent Component Analysis Based on Sliding Window Statistics and its Application to Power Plant Equipment State Monitoring

2013 ◽  
Vol 860-863 ◽  
pp. 1801-1806
Author(s):  
Yue Zhao ◽  
Feng Qi Si ◽  
Zhi Gao Xu
Keyword(s):  
Power Plant ◽  
Independent Component Analysis ◽  
Condition Monitoring ◽  
Sliding Window ◽  
Component Analysis ◽  
Independent Component ◽  
Engineering Practice ◽  
Monitoring Methods ◽  
Sliding Windows ◽  
Operation Conditions

Traditional condition monitoring methods are not suitable for the nonlinear operation parameters and time-variable operation conditions. We propose an independent component analysis method based on sliding window statistics (SSWICA). This method uses statistics in sliding windows of parameters as input samples, then uses a N-step forward sliding window ICA method to modeling. Then we monitor the operating state of the equipments by observing whether the SPE index of real-time parameters exceeds the control limits. SSWICA is applied to condition monitoring of condenser in 600MW unit, comparing with traditional ICA monitoring methods based on sliding window. The results show SSWICA can accurately reflect current operating state and related changes of condensers state parameters, recognize steady, unsteady and fault conditions effectively. It is valuable for engineering practice and suitable for the application to equipments condition monitoring in power plant.

scholarly journals Acoustical Source Tracing Using Independent Component Analysis and Correlation Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Cheng ◽  
Zhousuo Zhang ◽  
Jie Zhang ◽  
Jiantao Lu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Independent Component Analysis ◽  
Condition Monitoring ◽  
Component Analysis ◽  
Independent Component ◽  
Source Information ◽  
Independent Components ◽  
Source Tracing ◽  
Acoustical Signals

Acoustical signals from mechanical systems reveal the operating conditions of mechanical components and thus benefit for machinery condition monitoring and fault diagnosis. However, the acoustical signals directly measured by the sensors in essential are the mixed signals of all the sources, and normally it is very difficult to be used for source identification or operating feature extraction. Therefore, this paper studies the acoustical source tracing problem using independent component analysis (ICA) and identifies the sources using correlation analysis: the measured acoustical signals are separated into independent components by independent component analysis method, and thus all the independent information of all the sources is obtained; these independent components are identified based on the prior information of the sources and correlation analysis. Therefore, all the source information contained in the measured acoustical signals can be independently separated and traced, which can provide more purer source information for condition monitoring and fault diagnosis.

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

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.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
Sign in / Sign up

Export Citation Format

Share Document