scholarly journals An Optimum Balance Weight Search Algorithm

1993 ◽  
Author(s):  
James C. Austrow
Keyword(s):  
Search Algorithm ◽  
Least Square ◽  
Test Case ◽  
Influence Coefficient ◽  
Residual Vibration ◽  
Single Plane ◽  
Vibration Data ◽  
Influence Coefficients ◽  
Balance Weight ◽  
Optimum Balance

A mathematical description for an optimum balance weight search algorithm for single plane multipoint balance is presented. The algorithm uses influence coefficients, either measured or known beforehand, and measured complex vibration data to determine an optimum balance correction weight. The solution minimizes the maximum residual vibration. The algorithm allows user defined balance weights to be analyzed and evaluated. A test case is presented showing actual results and comparison with a least square solution algorithm. An efficient multiplane influence coefficient calculation scheme is also presented.

An Optimum Balance Weight Search Algorithm

1994 ◽  
Vol 116 (3) ◽  
pp. 678-681 ◽  
Author(s):  
J. C. Austrow
Keyword(s):  
Search Algorithm ◽  
Solution Algorithm ◽  
Test Case ◽  
Influence Coefficient ◽  
Residual Vibration ◽  
Single Plane ◽  
Vibration Data ◽  
Influence Coefficients ◽  
Balance Weight ◽  
Optimum Balance

A mathematical description for an optimum balance weight search algorithm for single-plane multipoint balance is presented. The algorithm uses influence coefficients, either measured or known beforehand, and measured complex vibration data to determine an optimum balance correction weight. The solution minimizes the maximum residual vibration. The algorithm allows user-defined balance weights to be analyzed and evaluated. A test case is presented showing actual results and comparison with a least-squares solution algorithm. An efficient multiplane influence coefficient calculation scheme is also presented.

An Optimal Shaft Balance Solution Using Integer Programming

2003 ◽  
Author(s):  
Eric Bechhoefer ◽  
Shawn Tayloe
Keyword(s):  
Integer Programming ◽  
Residual Vibration ◽  
Single Plane ◽  
Mathematical Solution ◽  
Vibration Data ◽  
Optimal Balance ◽  
Influence Coefficients ◽  
Balance Solution ◽  
Minimum Residual ◽  
Minimum Number

A mathematical solution for optimal balance weights is presented for single-plane, discrete weight and discrete adjustment point balance. The algorithm uses influence coefficients, either given or derived, and measured synchronous complex vibration data to determine the best adjustment. The solution has a user selected objective: minimum residual vibration or minimum number of adjustments to reach a given vibration. The algorithm is part of Goodrich’s Integrated Mechanical Diagnostics Health Usage Monitoring System (IMD HUMS), currently installed on a number of helicopter platforms.

Variation Simulation of Sheet Metal Assemblies Using the Method of Influence Coefficients With Contact Modeling

2006 ◽  
Vol 129 (3) ◽  
pp. 615-622 ◽  
Author(s):  
Stefan Dahlström ◽  
Lars Lindkvist
Keyword(s):  
Sheet Metal ◽  
Search Algorithm ◽  
Influence Coefficient ◽  
Contact Modeling ◽  
Element Analysis ◽  
Equilibrium Search ◽  
Contact Algorithm ◽  
Linear Relationships ◽  
Influence Coefficients ◽  
Geometric Quality

Sheet metal assembly is a common assembly process for several products such as automobiles and airplanes. Since all manufacturing processes are affected by variation, and products need to have a high geometric quality, geometry-related production problems must be analyzed during early design phases. This paper discusses two methods of performing this analysis. One way of performing the simulations relatively fast is to establish linear relationships between part deviation and assembly springback deviation by using the method of influence coefficient (MIC). However, this method does not consider contact between the parts. This means that the parts are allowed to penetrate each other which can affect the accuracy of the simulation result. This paper presents a simple contact modeling technique that can be implemented in to MIC to avoid penetrations. The contact modeling consists of a contact detection and a contact equilibrium search algorithm. When implemented, MIC still only requires two finite element analysis (FEA) calculations. This paper describes the steps in the contact algorithm and how it can be used in MIC, and finally the proposed contact modeling is verified by comparing the simulation result with commercial FEA software ABAQUS contact algorithm.

Application of Least Square Influential Coefficient Based on Particle Swarm Optimization in Dynamic Balancing Measuring

2011 ◽  
Vol 130-134 ◽  
pp. 3181-3184
Author(s):  
Chuan Jiang Li ◽  
Zi Qiang Zhang ◽  
Li Li Wan ◽  
Yi Li
Keyword(s):  
Particle Swarm Optimization ◽  
High Performance ◽  
Particle Swarm ◽  
Least Square ◽  
Experimental Result ◽  
Residual Vibration ◽  
Dynamic Balancing ◽  
Swarm Optimization ◽  
Balance Weight ◽  
Very High

This paper presents a least square influential coefficient based on particle swarm optimization, putting balance weight as optimizing object, which can make residual vibration meet the expected demand. Experimental result shows that this method has high performance optimizing effect, and high percent of removed unbalance amount one correction, which is over 95%. So this method has very high practical value.

Two-Plane Balancing of a Rotor System Without Phase Response Measurements

1987 ◽  
Vol 109 (2) ◽  
pp. 162-167 ◽  
Author(s):  
Louis J. Everett
Keyword(s):  
Phase Measurement ◽  
Influence Coefficient ◽  
Single Plane ◽  
Flexible Rotors ◽  
Influence Coefficients ◽  
Wide Range ◽  
Influence Coefficient Method ◽  
Require Response ◽  
Rigid Rotors ◽  
Coefficient Method

This paper presents, and experimentally verifies, a two-plane balancing technique for rigid rotors and possibly flexible rotors operating at a constant speed. The technique, based upon influence coefficients, extends the single-plane four-run balancing procedure to two planes. Like the four-run method, this technique is most easily performed graphically and does not require response phase measurement. Despite the additional runs required to obtain data, its simplicity and applicability to a wide range of equipment renders it more useful, in some cases, than the standard two-plane influence coefficient method.

Methods and Procedures for Trim Balancing the LM2500 Marine Gas Turbine in the Test Cell and Aboard Ship

1989 ◽  
Author(s):  
Bruce D. Thompson ◽  
Robert H. Badgley ◽  
Richard Raczkowski
Keyword(s):  
Gas Turbines ◽  
Angle Measurement ◽  
Influence Coefficient ◽  
Gas Generator ◽  
Residual Vibration ◽  
Successful Development ◽  
Generator Rotor ◽  
Practical Procedure ◽  
Influence Coefficients ◽  
Speed Range

Extensive fleet experience with LM2500 marine gas turbines shows that engines with higher than normal vibration are more likely to show early wear. Gas generator rotor unbalance has been identified as the main cause of high vibration. Rotor rebalancing reduces vibration to acceptable levels, at the same time reducing or eliminating many wearout modes. Initially, the only rebalance option was to remove the gas generator from the ship and send it to the depot. The high cost of this option led to a search for alternatives, and the successful development of a procedure for rebalancing the gas generator rotor aboard ship. The method adopted was the well known influence coefficient procedure, developed by the National Aeronautics and Space Administration (NASA) in the late 1960’s. This method is well suited for implementation on portable computers, and fits readily into a practical procedure for use by trained technicians. The NASA program originally included a procedure to minimize peak residual vibration. Navy engineers added an improved optimizing procedure and a method to account for engine nonlinearities. Rebalancing involves mounting four external accelerometers on the engine, along with a tachometer to give a one-per-rev signal for phase angle measurement. Baseline vibration measurements, together with stored influence coefficients for the LM2500 engine series, permit first shot multi-plane, multi-speed trim correction weights to be calculated. The compressor case is readily opened and the weights installed without disturbing the engine. Application of this procedure has been highly successful: vibration levels of less than 0.001 inch peak-to-peak over the entire speed range have been achieved. The avoided cost of removal, replacement and repair of an LM2500 is estimated to be about $500,000.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

Study on Single-Plane Biaxial Balance Monitor System in ULTRA-Precision Grinding

2006 ◽  
Vol 304-305 ◽  
pp. 251-255
Author(s):  
L. Zheng ◽  
Yin Biao Guo ◽  
Z.Z. Wang
Keyword(s):  
Mutual Influence ◽  
Machining Accuracy ◽  
Monitor System ◽  
Machining Parameters ◽  
Precision Grinding ◽  
Single Plane ◽  
Workpiece Surface ◽  
Surface Error ◽  
Vibration Data ◽  
Ultra Precision

This paper puts forward an intelligent single-plane biaxial balance monitor system, which is used in ultra-precision grinding. It adopts the method of single-plane balance correction for the vibration of wheel and workpiece. And this system can also be used for integral balance. For ultra-precision grinding, caused by the mutual influence of the vibration of wheel and workpiece, there will be a ripple on the workpiece surface, which is mainly influenced by the frequency ratio of wheel to workpiece, the feed rate and the vibration of wheel and workpiece. This system can improve the machining accuracy, reduce the surface error of workpiece and appraise the integrated machining result, by analyzing the vibration data of wheel and workpiece and adjusting machining parameters.

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