Non recursive Nonlinear Least Squares for periodic signal fitting

Giarnetti, Sabino; Leccese, Fabio; Caciotta, Maurizio

doi:10.1016/j.measurement.2017.02.023

Cited by 17 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…У роботі [20] представлено нерекурсивну версію нелінійної апроксимації методом найменших квадратів для оцінки частоти. Запропонований спосіб вимагає попередньої оцінки частоти.…”

Section: вступunclassified

Approximation of Non-Periodic Signals in Systems of Multi-Class Diagnostics

Tsybulnyk¹,

Komenchuk²

2019

BNTUU"KPI".SIM

View full text Add to dashboard Cite

Section: вступunclassified

Approximation of Non-Periodic Signals in Systems of Multi-Class Diagnostics

Tsybulnyk¹,

Komenchuk²

2019

BNTUU"KPI".SIM

View full text Add to dashboard Cite

“…The descriptions of the methods and systems for electrical power quality assessment, in their various aspects, can be found, e.g., in [22][23][24][25][26][27].…”

Section: No Type Of Instrument Ownermentioning

confidence: 99%

Validation of the Measurement Characteristics in an Instrument for Power Quality Estimation—A Case Study

Masnicki

2017

Energies

View full text Add to dashboard Cite

An acceptable quality of electrical energy is seen today as an important component of ecology. Several instruments for estimating the quality of electrical power have been elaborated. Each supplier assures that the instrument meets the applicable standards and that the uncertainty of the measurement results obtained using the instrument does not exceed the established levels. The accuracy of the measurement results depends on a couple of things, e.g., the correctness of the measurement algorithms implemented in the instrument and the quality of its calibration. In this paper, the basic features of an "estimator/analyzer" (E/A) instrument, as well as the calibration methods of the instrument, the verification of its measurement algorithms, and also the obtained exemplary results, are shown. The proposal of the strategy of the reliable validation of embedded measurement algorithms for the identification of parameters characterizing electrical power quality in the power grid is discussed.

show abstract

“…Curve fitting refers to trying to find a smooth curve that best fits the data. Curve fitting must describe the intrinsic relationship between the two variables, time and rotational speed, and should reflect the trends of these discrete data well [14]. Here, we must find a curve that fits the discrete points well, and then use the curve equation to obtain the final acceleration time.…”

Section: Curve Fitting and Interpolation Of Discrete Velocity Pointsmentioning

confidence: 99%

“…The curve fitting method mainly involves choosing a suitable curve type, carrying out the variable transformation, solving the linear equation, and finally transforming the linear equation into a function. In the field of system identification, the least squares method is the most commonly used method for curve fitting, because it is highly accurate and practical [14,15].…”

Section: Curve Fitting and Interpolation Of Discrete Velocity Pointsmentioning

confidence: 99%

Engine power measurement without external load based on vibration signal

Zhang¹,

Zeng²

2017

J. vibroeng.

View full text Add to dashboard Cite

The vibration signals from engines contain information about the engine conditions. Engine power is an important parameter that indicates the state of an engine. In this manuscript, a method for measuring the engine power, using the vibration signals from the engine, is developed. In our proposed method, the power of the engine is measured based on the principle of no-load measurement. The discrete values of rotation speed are computed from the vibration signal, using wavelet transforms, the Hilbert envelope, and three autocorrelations. By comparing various curve-fitting and interpolation methods, an optimal method for curve fitting the discrete acceleration data is chosen, and the corresponding times of the starting speed and terminal speed are calculated using the analytical formulas of the fitted curve. Finally, the maximum engine power is obtained from the calibration tests. Experimental results show that the overall performance is good and that the maximum power measurement accuracy is 98.4 % after accurate calibration. This meets the requirements of the power measurement accuracy under the condition of no load.

show abstract

Non recursive Nonlinear Least Squares for periodic signal fitting

Cited by 17 publications

References 18 publications

Approximation of Non-Periodic Signals in Systems of Multi-Class Diagnostics

Approximation of Non-Periodic Signals in Systems of Multi-Class Diagnostics

Validation of the Measurement Characteristics in an Instrument for Power Quality Estimation—A Case Study

Engine power measurement without external load based on vibration signal

Contact Info

Product

Resources

About