Vibrations measurements for shrouded blades of steam turbines based oneddy current sensors with high frequency response

With the development of power plants towards high power and intelligent operation direction,the vibrations or failures of blades,especially the last stage blades in steam turbines,happen more frequently due to the unstable operating conditions brought by flexible operation.A vibration measuring method for the shrouded blades of a steam turbine based on eddy current sensors with high frequency response is proposed,meeting the requirements of non-contact heath monitoring.The eddy current sensors produce the signals which are related to the area changing of every blade’s shroud resulting from the rotation of stator.Then an improved blade tip timing(BTT)technique is proposed to detect the vibrations of shrouded blades by measuring the arrival time of each area changing signal.A structure of eddy current sensors is developed in steam turbines and an amplitude modulation/demodulation circuit is designed to improve the response bandwidth up to 250 kHz.Vibration tests for the last stage blades of a steam turbine were carried out and the results validate the efficiency of the improved BTT technique and the high frequency response of the eddy current sensors presented.

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.

A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

In this paper,we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).

Parameter Optimization and Precision Enhancement of Dual-Coil Eddy Current Sensor

To enhance the measurement precision of eddy current sensor in particular environments such asextreme temperature changes and limited available space in aerospace, we optimized the structural parameters ofthe traditional dual-coil eddy current sensor probe by electromagnetic field analysis and finite element simulationmodeling, and further presented the criteria for determining the optimal coil distance of the dual-coil probe. Thesimulation results are verified by setting up an experimental platform. For the extreme temperature environment,the displacement measurement error caused by the full range temperature variation of the dual-coil sensor underthe optimal distance is less than 21.0% of that of the single-coil sensor. On this basis, we analyzed and verified thethermal stability of the structurally optimized dual-coil eddy current sensor. After temperature compensation,the displacement measurement accuracy can reach 14.9 times more accurate than that of the single-coil sensor.The method proposed in this paper can provide a design reference for the structural optimization of the axialdual-coil eddy current sensor probe.

Parameters optimization and nonlinearity analysis of grating eddy current displacement sensor using neural network and genetic algorithm

A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.

Characteristics analysis and parameters optimization for the grating eddy current displacement sensor

The grating eddy current displacement sensor (GECDS) for distance or position measurement used in watertight electronic calipers was described. The sensor relies on repetitive variation of inductance against displacement caused by the change of coupling areas between moving coils and static reflectors. The investigations focused on setting up and utilizing a computer model of the 3D eddy current fields and geometry to analyze causes of the production of measurement blind areas, and to investigate effects of the sensor parameters, such as axial gap between coils and reflectors, reflector length and reflector width on characteristics of the sensor. Simulation results indicated that the sensor has the smallest nonlinearity error of 0.15%, which agrees well with the experimental results.

HIGH TEMPERATURE DISPLACEMENT SENSOR

A high temperature displacement sensor based on the principle of eddy-current is investigated. A new temperature compensation technique by using eddy-current effect is presented to satisfy the special requirement at high temperature up to 550℃. The experiment shows that the temperature compensation technique leads to good temperature stability for the sensors. The variation of the sensitivity as well as the temperature drift of the sensor with temperature compensation technique is only about 7.4% and 90-350 mV at 550 ℃ compared with that at room temperature, and that of the sensor without temperature compensation technique is about 31.2% and 2-3 V at 550 ℃ compared with that at room temperature. A new dynamic calibration method for the eddy-current displacement sensor is presented, which is very easy to be realized especially in high frequency and at high temperatures. The high temperature displacement sensors developed are successfully used at temperature up to 550 ℃ in a magnetic bearing system for more than 100 h.

Investigating Averaging Effect by Using Three Dimension Spectrum

The eddy current displacement sensor＇s averaging effect has been investigated in this paper, and the frequency spectrum property of the averaging effect was also deduced. It indicates that the averaging effect has no influences on measuring a rotor＇s rotating error, but it has visible influences on measuring the rotor＇s profile error. According to the frequency spectrum of the averaging effect, the actual sampling data can be adjusted reasonably, thus measuring precision is improved.