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 pa...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.展开更多
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 displacem...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.展开更多
文摘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.
文摘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.
