Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Kenneth Kar ◽  
Akshya K. Swain ◽  
Robert Raine
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Time Constant ◽  
Identification Problem ◽  
Basis Functions ◽  
New Technique ◽  
Ratio Test ◽  
Time Varying ◽  
Time Constants ◽  
Orthogonal Least Squares

The present study addresses the problem of estimating time-varying time constants associated with thermocouple sensors by a set of basis functions. By expanding each time-varying time constant onto a finite set of basis sequences, the time-varying identification problem reduces to a parameter estimation problem of a time-invariant system. The proposed algorithm, to be called as orthogonal least-squares with basis function expansion algorithm, combines the orthogonal least-squares algorithm with an error reduction ratio test to include significant basis functions into the model, which results in a parsimonious model structure. The performance of the method was compared with a linear Kalman filter. Simulations on engine data have demonstrated that the proposed method performs satisfactorily and is better than the Kalman filter. The new technique has been applied in a Stirling cycle compressor. The sinusoidal variations in time constant are tracked properly using the new technique, but the linear Kalman filter fails to do so. Both model validation and thermodynamic laws confirm that the new technique gives unbiased estimates and that the assumed thermocouple model is adequate.

scholarly journals IDENTIFICATION OF EXCITABLE MEDIA USING A SCALAR COUPLED MAP LATTICE MODEL

2010 ◽  
Vol 20 (07) ◽  
pp. 2137-2150 ◽  
Author(s):  
YUZHU GUO ◽  
YIFAN ZHAO ◽  
S. A. BILLINGS ◽  
DANIEL COCA ◽  
R. I. RISTIC ◽  
...  
Keyword(s):  
Least Squares ◽  
Lattice Model ◽  
Identification Problem ◽  
Coupled Map Lattice ◽  
Excitable Media ◽  
Local Dynamics ◽  
Orthogonal Least Squares ◽  
Least Squares Algorithm ◽  
Belousov Zhabotinsky Reaction

The identification problem for excitable media is investigated in this paper. A new scalar coupled map lattice (SCML) model is introduced and the orthogonal least squares algorithm is employed to determinate the structure of the SCML model and to estimate the associated parameters. A simulated pattern and a pattern observed directly from a real Belousov–Zhabotinsky reaction are identified. The identified SCML models are shown to possess almost the same local dynamics as the original systems and are able to provide good long term predictions.

scholarly journals Meson bunches formed by pulsed laser-induced processes in ultra-dense hydrogen H(0)

2021 ◽  
Author(s):  
Leif Holmlid
Keyword(s):  
Time Constant ◽  
Decay Time ◽  
Charged Pion ◽  
Decay Time Constant ◽  
Time Varying ◽  
Charged Kaon ◽  
Time Constants ◽  
Nuclear Process ◽  
Current Densities ◽  
Nuclear Processes

Abstract Ultra-dense hydrogen H(0) (reviewed in Holmlid and Zeiner-Gundersen, Physica Scripta 2019 ) consists of small strongly bound molecules with interatomic distance of 0.56 pm in spin state s = 1. It is a useful nuclear fuel for energy generation, giving heat above break-even (Holmlid, AIP Advances 2015) in laser-induced processes (Holmlid, Int. J. Hydr. Energy 2021). Nuclear processes in H(0) emit particles in typical meson decay chains with kinetic energy up to 100 MeV. These mesons decay and generate fast muons at up to 500 MeV energy at current densities of several mA cm-2 at 1–2 m distances, which corresponds to 1013 -1014 muons formed per laser pulse. It is shown that the mesons decay in chain processes with well-defined meson time constants in the range 10–60 ns. The time varying signals from H(0) agree well with mesons M in decay chains as A ◊ M ◊ N where N is a signal muon. M may be a charged kaon K± (decay time constant at rest 12.4 ns) or a charged pion π± (decay time constant at rest 26 ns) or a long-lived neutral kaon \({\text{K}}_{L}^{0}\) (decay time constant at rest 51 ns). Ultra-dense protium p(0) gives the same time constants as D(0) but slightly different decay-chains. The meson bunches observed are similar to the meson bunches from nucleon + antinucleon annihilation. The energy gain in the nuclear process is at least 8000, strongly indicating baryon annihilation for which process further evidence is given in other recent publications.

scholarly journals Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402093046
Author(s):  
Siyi Chen ◽  
Jubin Lu ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Parametric Identification ◽  
Identification Problem ◽  
Structural Systems ◽  
Wavelet Expansion ◽  
Time Varying ◽  
Multiresolution Decomposition ◽  
Original Time

Structural systems often exhibit time-varying dynamic characteristics during their service life due to serve hazards and environmental erosion, so the identification of time-varying structural systems is an important research topic. Among the previous methodologies, wavelet multiresolution analysis for time-varying structural systems has gained increasing attention in the past decades. However, most of the existing wavelet-based identification approaches request the full measurements of structural responses including acceleration, velocity, and displacement responses at all dynamic degrees of freedom. In this article, an improved algorithm is proposed for the identification of time-varying structural parameters using only partial measurements of structural acceleration responses. The proposed algorithm is based on the synthesis of wavelet multiresolution decomposition and the Kalman filter approach. The time-varying structural stiffness and damping parameters are expanded at multi-scale profile by wavelet multiresolution decomposition, so the time-varying parametric identification problem is converted into a time-invariant one. Structural full responses are estimated by Kalman filter using partial observations of structural acceleration responses. The scale coefficients by the wavelet expansion are estimated via the solution of a nonlinear optimization problem of minimizing the errors between estimated and observed accelerations. Finally, the original time-varying parameters can be reconstructed. To demonstrate the efficiency of the proposed algorithm, the identification of several numerical examples with various time-varying scenarios is studied.

scholarly journals Time-Varying Nonlinear Causality Detection Using Regularized Orthogonal Least Squares and Multi-Wavelets With Applications to EEG

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 17826-17840 ◽  
Author(s):  
Yang Li ◽  
Meng-Ying Lei ◽  
Yuzhu Guo ◽  
Zhongyi Hu ◽  
Hua-Liang Wei
Keyword(s):  
Least Squares ◽  
Time Varying ◽  
Orthogonal Least Squares

On robust Kalman filter for two-dimensional uncertain linear discrete time-varying systems: A least squares method

Automatica ◽  
2019 ◽  
Vol 99 ◽  
pp. 203-212 ◽  
Author(s):  
Dong Zhao ◽  
Steven X. Ding ◽  
Hamid Reza Karimi ◽  
Yueyang Li ◽  
Youqing Wang
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Discrete Time ◽  
Least Squares Method ◽  
Time Varying ◽  
Two Dimensional ◽  
Robust Kalman Filter ◽  
Time Varying Systems
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