Estimation of Sensor Array Signal Model Parameters Using Factor Analysis

2019 ◽  
Author(s):  
Andreas I. Koutrouvelis ◽  
Richard C. Hendriks ◽  
Richard Heusdens ◽  
Jesper Jensen
Keyword(s):  
Factor Analysis ◽  
Sensor Array ◽  
Model Parameters ◽  
Signal Model

scholarly journals Methods for the Calculation of Thermoacoustic Stability Margins and Monte Carlo-Free Uncertainty Quantification

2017 ◽  
Author(s):  
Georg A. Mensah ◽  
Luca Magri ◽  
Jonas P. Moeck
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Risk Factor ◽  
Uncertainty Quantification ◽  
Frequency Domain ◽  
Gas Turbines ◽  
Network Models ◽  
System Stability ◽  
Risk Factor Analysis ◽  
Model Parameters

Thermoacoustic instabilities are a major threat for modern gas turbines. Frequency-domain based stability methods, such as network models and Helmholtz solvers, are common design tools because they are fast compared to compressible CFD computations. Frequency-domain approaches result in an eigenvalue problem, which is nonlinear with respect to the eigenvalue. Nonlinear functions of the frequency are, for example, the n–τ model, impedance boundary conditions, etc. Thus, the influence of the relevant parameters on mode stability is only given implicitly. Small changes in some model parameters, which are obtained by experiments with some uncertainty, may have a great impact on stability. The assessment of how parameter uncertainties propagate to system stability is therefore crucial for safe gas turbine operation. This question is addressed by uncertainty quantification. A common strategy for uncertainty quantification in thermoacoustics is risk factor analysis. It quantifies the uncertainty of a set of parameters in terms of the probability of a mode to become unstable. One general challenge regarding uncertainty quantification is the sheer number of uncertain parameter combinations to be quantified. For instance, uncertain parameters in an annular combustor might be the equivalence ratio, convection times, geometrical parameters, boundary impedances, flame response model parameters etc. Assessing also the influence of all possible combinations of these parameters on the risk factor is a numerically very costly task. A new and fast way to obtain algebraic parameter models in order to tackle the implicit nature of the eigenfrequency problem is using adjoint perturbation theory. Though adjoint perturbation methods were recently applied to accelerate the risk factor analysis, its potential to improve the theory has not yet been fully exploited. This paper aims to further utilize adjoint methods for the quantification of uncertainties. This analytical method avoids the usual random Monte Carlo simulations, making it particularly attractive for industrial purposes. Using network models and the open-source Helmholtz solver PyHoltz it is also discussed how to apply the method with standard modeling techniques. The theory is exemplified based on a simple ducted flame and a combustor of EM2C laboratory for which experimental validation is available.

Parallel Factor Analysis for Gas Sensor Array Signals

2014 ◽  
Vol 494-495 ◽  
pp. 955-959 ◽  
Author(s):  
Wen Na Zhang ◽  
Guo Jun Qin ◽  
Niao Qing Hu
Keyword(s):  
Factor Analysis ◽  
Gas Sensor ◽  
Sensor Array ◽  
Parallel Factor Analysis ◽  
Time Profile ◽  
Three Dimension ◽  
Gas Sensor Array ◽  
Parallel Factor ◽  
Sample Time

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.

A novel approach to extracting small-signal model parameters of silicon MOSFET's

1997 ◽  
Vol 7 (3) ◽  
pp. 75-77 ◽  
Author(s):  
S. Lee ◽  
H.K. Yu ◽  
C.S. Kim ◽  
J.G. Koo ◽  
K.S. Nam
Keyword(s):  
Model Parameters ◽  
Small Signal ◽  
Signal Model ◽  
Small Signal Model ◽  
Novel Approach

Extraction of temperature dependences of small-signal model parameters in SiGe HBT HICUM model

2016 ◽  
Vol 25 (4) ◽  
pp. 048501 ◽  
Author(s):  
Ya-Bin Sun ◽  
Jun Fu ◽  
Yu-Dong Wang ◽  
Wei Zhou ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Model Parameters ◽  
Small Signal ◽  
Signal Model ◽  
Sige Hbt ◽  
Small Signal Model ◽  
Temperature Dependences
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