Reduction of Noise Effects for In Situ Balancing of Rotors

2005 ◽  
Vol 127 (3) ◽  
pp. 234-246 ◽  
Author(s):  
S. D. Garvey ◽  
E. J. Williams ◽  
G. Cotter ◽  
C. Davies ◽  
N. Grum
Keyword(s):  
Least Squares ◽  
Rotational Speed ◽  
Standard Procedure ◽  
Least Square ◽  
Measurement Noise ◽  
Rotor Speed ◽  
Strong Argument ◽  
Noise Effects ◽  
Low Levels

Precision balancing any given rotor for smooth running at one or more fixed speeds requires that unbalance-originated deflections and/or forces be measured. When the rotor is balanced in situ, the standard procedure is to conduct trial runs to obtain all of the requisite information. These include at least one run with the rotor in its uncorrected state and then, for each balancing plane on the rotor, at least one run with a trial balance mass fitted to that plane. Synchronous components of deflection and/or force may be measured at numerous transducers on the machine. If the machine has B short bearings, then at any one rotor speed, the dimension of the space spanned by all of the complex vectors of synchronous stator response from the trial runs should be (at most) B. In practice, the dimension of that space is very often larger. This paper firstly demonstrates how minimal adjustments can be applied to the (complex) measured synchronous stator vibration vectors to force those vectors into a space of dimension B. Standard least-square methods can then be applied to discover a suitable set of unbalance corrections. The paper shows that in virtually all cases where the noise is high, applying this procedure of projecting the data from a given rotational speed into a space of appropriate dimension before computing the least-squares calculation is beneficial. Reductions in the balancing cost function (the scalar quantity minimized by the least squares calculation) by factors of 2 to 4 are typically obtained for realistic levels of measurement noise. At very low levels of noise, the procedure is neither beneficial nor harmful. There is a strong argument that it should always be applied.

scholarly journals MPPT Control Strategy of PV Based on Improved Shuffled Frog Leaping Algorithm under Complex Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaohua Nie ◽  
Haoyao Nie
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Least Square ◽  
Measurement Noise ◽  
Complex Environments ◽  
Recursive Least Square ◽  
Shuffled Frog Leaping Algorithm ◽  
Noise Effects ◽  
Pv Array ◽  
Shuffled Frog Leaping

This work presents a maximum power point tracking (MPPT) based on the particle swarm optimization (PSO) improved shuffled frog leaping algorithm (PSFLA). The swarm intelligence algorithm (SIA) has vast computing ability. The MPPT control strategies of PV array based on SIA are attracting considerable interests. Firstly, the PSFLA was proposed by adding the inertia weight factor w of PSO in standard SFLA to overcome the defect of falling into the partial optimal solutions and slow convergence speed. The proposed PSFLA algorithm increased calculation speed and excellent global search capability of MPPT. Then, the PSFLA was applied to MPPT to solve the multiple extreme point problems of nonlinear optimization. Secondly, for the problems of MPPT under complex environments, a new MPPT strategy of the PSFLA combined with recursive least square filtering was proposed to overcome the measurement noise effects on MPPT accuracy. Finally, the simulation comparisons between PSFLA and SFLA algorithm were developed. The experiment and comparison between PSLFA and PSO algorithm under complex environment were executed. The simulation and experiment results indicate that the proposed MPPT control strategy based on PSFLA can suppress the measurement noise effects effectively and improve the PV array efficiency.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

Application of Fitting Estimation Method in GPS Height Fitting

2013 ◽  
Vol 694-697 ◽  
pp. 2545-2549 ◽  
Author(s):  
Qian Wen Cheng ◽  
Lu Ben Zhang ◽  
Hong Hua Chen
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Least Square ◽  
Least Squares Collocation ◽  
Normal Height ◽  
Geodetic Height ◽  
The Given ◽  
Surveying And Mapping

The key point researched by many scholars in the field of surveying and mapping is how to use the given geodetic height H measured by GPS to obtain the normal height. Although many commonly-used fitting methods have solved many problems, they all value the pending parameters as the nonrandom variables. Figuring out the best valuations, according to the traditional least square principle, only considers its trend or randomness, which is theoretically incomprehensive and have limitations in practice. Therefore, a method is needed not only considers its trend but also takes randomness into account. This method is called the least squares collocation.

Identification of Bearing Coefficients of Flexible Rotor-Bearing Systems

2000 ◽  
Author(s):  
Tachung Yang ◽  
Wei-Ching Chaung
Keyword(s):  
Industrial Applications ◽  
Operating Conditions ◽  
Least Square ◽  
Flexible Rotor ◽  
Fem Modeling ◽  
Identification Method ◽  
Reaction Forces ◽  
Manufacturing Assembly ◽  
Stiffness And Damping

The accuracy of stiffness and damping coefficients of bearings is critical for the rotordynamic analysis of rotating machinery. However, the influence of bearings depends on the design, manufacturing, assembly, and operating conditions of the bearings. Uncertainties occur quite often in manufacturing and assembly, which causes the inaccuracy of bearing predictions. An accurate and reliable in-situ identification method for the bearing coefficients is valuable to both analyses and industrial applications. The identification method developed in this research used the receptance matrices of flexible shafts from FEM modeling and the unbalance forces of trial masses to derive the displacements and reaction forces at bearing locations. Eight bearing coefficients are identified through a Total Least Square (TLS) procedure, which can handle noise effectively. A special feature of this method is that it can identify bearing coefficients at a specific operating speed, which make it suitable for the measurement of speed-dependent bearings, like hydrodynamic bearings. Numerical validation of this method is presented. The configurations of unbalance mass arrangements are discussed.

Least‐squares inversion of in‐situ sonic Q measurements: Stability and resolution

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 378-385 ◽  
Author(s):  
Aristotelis Dasios ◽  
Clive McCann ◽  
Timothy Astin
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Instantaneous Frequency ◽  
Spectral Ratio ◽  
Full Waveform ◽  
Multiple Measurements ◽  
Sandstone Reservoir ◽  
Gas Bearing

We minimize the effect of noise and increase both the reliability and the resolution of attenuation estimates obtained from multireceiver full‐waveform sonics. Multiple measurements of effective attenuation were generated from full‐waveform sonic data recorded by an eight‐receiver sonic tool in a gas‐bearing sandstone reservoir using two independent techniques: the logarithmic spectral ratio (LSR) and the instantaneous frequency (IF) method. After rejecting unstable estimates [receiver separation <2 ft (0.61 m)], least‐squares inversion was used to combine the multiple estimates into high‐resolution attenuation logs. The procedure was applied to raw attenuation data obtained with both the LSR and IF methods, and the resulting logs showed that the attenuation estimates obtained for the maximum receiver separation of 3.5 ft (1.07 m) provide a smoothed approximation of the high‐resolution measurements. The approximation is better for the IF method, with the normalized crosscorrelation factor between the low‐ and high‐resolution logs being 0.90 for the IF method and 0.88 for the LSR method.

Research on the Wire Drawing Die Pass Parameter Calculation Based on the Least Square Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 850-853
Author(s):  
Huai Xing Wen ◽  
Yong Tao Yang
Keyword(s):  
Pore Structure ◽  
Least Squares ◽  
Least Squares Method ◽  
Piecewise Linear ◽  
Wire Drawing ◽  
Least Square ◽  
Structure Characteristics ◽  
Drawing Die ◽  
The Wire ◽  
Drawing Dies

Drawing Dies meter A / D acquisition module will be collected from the mold hole contour data to draw a curve in Matlab. According to the mold pore structure characteristics of the curve, the initial cut-off point of each part of contour is determined and iteratived optimization to find the best cut-off point, use the least squares method for fitting piecewise linear and fitting optimization to find the function of the various parts of the curve function, finally calculate the pass parameters of drawing mode. Parameters obtained compare with the standard mold, both of errors are relatively small that prove the correctness of the algorithm. Also a complete algorithm flow of pass parameters is designed, it can fast and accurately measure the wire drawing die hole parameters.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

scholarly journals PREDICTION OF PASSENGER FLOW ON THE HIGHWAY BASED ON THE LEAST SQUARE SUPPOERT VECTOR MACHINE / MAŽIAUSIŲ KVADRATŲ ATRAMINIŲ VEKTORIŲ METODO TAIKYMAS KELEIVIŲ SRAUTUI GREITKELYJE PROGNOZUOTI / ПРИМЕНЕНИЕ МЕТОДА ОПОРНЫХ ВЕКТОРОВ С КВАДРАТИЧНОЙ ФУНКЦИЕЙ ПОТЕРЬ ДЛЯ ПРОГНОЗИРОВАНИЯ ПАССАЖИРСКИХ ПОТОКОВ НА АВТОМАГИСТРАЛЯХ

Transport ◽  
2011 ◽  
Vol 26 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yanrong Hu ◽  
Chong Wu ◽  
Hongjiu Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Least Square ◽  
Small Samples ◽  
Support Vector ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Passenger Flow ◽  
Regressive Model ◽  
Structural Risk

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway. Santrauka Atraminių vektorių metodas (Support Vector Machine – SVM) yra skaičiuojamasis metodas, paremtas statistikos teorija, struktūriniu požiūriu mažinant riziką. SVM metodas, palyginti su kitais metodais, yra patikimesnis metodas, nes juo remiantis galima išspręsti realias problemas, esant įvairioms sąlygoms. Tyrimams naudojama SVM metodo regresijos teorija ir sukuriamas regresinis modelis, kuris grindžiamas mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector Machine – LS-SVM). Straipsnio autoriai prognozuoja keleivių srautą Hangdžou (Kinija) greitkelyje 2000–2008 m. Gauti rezultatai rodo, kad regresinis LS-SVM modelis yra labai tikslus ir patikimas, todėl gali būti efektyviai taikomas keleivių srautams prognozuoti greitkeliuose. Резюме Метод опорных векторов (Support Vector Machine – SVM) – это набор аналогичных алгоритмов вида «обучение с учителем», использующихся для задач классификации и регрессионного анализа. Метод SVM принадлежит к семейству линейных классификаторов. Основная идея метода SVM заключается в переводе исходных векторов в пространство более высокой размерности и поиске разделяющей гиперплоскости с максимальным зазором в этом пространстве. Алгоритм работает в предположении, что чем больше разница или расстояние между параллельными гиперплоскостями, тем меньше будет средняя ошибка классификатора. В сравнении с другими методами метод SVM более надежен и позволяет решать проблемы с различными условиями. Для исследования был использован метод SVM и регрессионный анализ, затем создана регрессионная модель, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector Machine – LS-SVM). Авторы прогнозировали пассажирский поток на автомагистрали Ханчжоу (Китай) в 2000–2008 гг. Полученные результаты показывают, что регрессионная модель LS-SVM является надежной и может быть применена для прогнозирования пассажирских потоков на других магистралях.

Sign in / Sign up

Export Citation Format

Share Document